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Python Ray – Transforming the way to Distributed Computing

Python ray feature

Scale your most complex AI and Python workloads with Ray, a simple yet powerful Parallel and distributed computing framework.

Can you imagine the pain of training complex machine learning models that take days or even months depending on the amount of data you have? What if you can train those models within minutes to a maximum of a few hours? Impressive, right? Who does not want that? 

But the question is how?

This is where Python Ray comes to your rescue and helps you train models with great efficiency. Ray is a superb tool for effective distributed Python to speed up data processing and Machine Learning workflows. It leverages several CPUs and machines that process the code parallelly and process all the data at lightening fast speed.

This comprehensive Python Ray guide will help you understand its potential usage and how it can help ML platforms to work efficiently.

Let’s get you started.

What is Ray?

Ray is an open-source framework designed to scale AI and Python applications, including machine learning. It simplifies the process of parallel processing, eliminating the need for expertise in distributed systems. Ray gained immense popularity in quick time.

Do you know that top companies are leveraging Ray? Prominent companies such as Uber, Shopify, and Instacart utilize Ray. 

Spotify Leveraging Ray

Ray helps Spotify’s data scientists and engineers access a wide range of Python-based libraries to manage their ML workload.

Spotify Leveraging Ray

Image Credit: Anyscale

Understanding Ray Architecture

  • The head node in a Ray cluster has additional components compared to worker nodes.
  • The Global Control Store (GCS) stores cluster-wide information, including object tables, task tables, function tables, and event logs. It is used for web UI, error diagnostics, debugging, and profiling tools.
  • The Autoscaler is responsible for launching and terminating worker nodes to ensure sufficient resources for workloads while minimizing idle resources.
  • The head node serves as a master that manages the entire cluster through the Autoscaler. However, the head node is a single point of failure. If it is lost, the cluster needs to be re-created, and existing worker nodes may become orphans and require manual removal.
  • Each Ray node contains a Raylet, which consists of two main components: the Object Store and the Scheduler.
  • The Object Store connects all object stores together, similar to a distributed cache like Memcached.
  • The Scheduler within each Ray node functions as a local scheduler that communicates with other nodes, creating a unified distributed scheduler for the cluster.

In a Ray cluster, nodes refer to logical nodes based on Docker images rather than physical machines. A physical machine can run one or more logical nodes when mapping to the physical infrastructure.

Ray Framework

It is possible with the help of the following low-level and high-level layers. Ray framework lets you scale AI and Python apps. It comes with a core distributed runtime and set of libraries (Ray AIR) that simplifies ML computations.

Ray Framework

Image Credits: Ray

  • Scale ML workloads (Ray AI Runtime)- Ray provides ready-to-use libraries for common machine learning tasks such as data preprocessing, distributed training, hyperparameter tuning, reinforcement learning, and model serving.
  • Build Distributing Apps (Ray Core)- It offers user-friendly tools for parallelizing and scaling Python applications, making it easy to distribute workloads across multiple nodes and GPUs.
  • Deploy large-scale workloads (Ray Cluster)- Ray clusters consist of multiple worker nodes that are connected to a central Ray head node. These clusters can be configured to have a fixed size or can dynamically scale up or down based on the resource requirements of the applications running on the cluster. Ray seamlessly integrates with existing tools and infrastructure like Kubernetes, AWS, GCP, and Azure, enabling the smooth deployment of Ray clusters.

Ray and Data Science Workflow and Libraries

The concept of “data science” has evolved in recent years and can have different definitions. In simple terms, data science is about using data to gain insights and create practical applications. If we consider ML, then it involves a series of steps.

Data Processing–

Preparing the data for machine learning, if applicable. This step involves selecting and transforming the data to make it compatible with the machine learning model. Reliable tools can assist with this process.

Model Training-

Training machine learning algorithms using the processed data. Choosing the right algorithm for the task is crucial. Having a range of algorithm options can be beneficial.

Hyperparameter Tuning–

Fine-tuning parameters and hyperparameters during the model training process to optimize performance. Proper adjustment of these settings can significantly impact the effectiveness of the final model. Tools are available to assist with this optimization process.

Model Serving–

Deploying trained models to make them accessible for users who need them. This step involves making the models available through various means, such as using HTTP servers or specialized software packages designed for serving machine learning models.

Ray has developed specialized libraries for each of the four machine-learning steps mentioned earlier. These libraries are designed to work seamlessly with Ray and include the following.

Ray Datasets-

This library facilitates data processing tasks, allowing you to efficiently handle and manipulate datasets. It supports different file formats and store data as blocks rather than a single block. Best used for data processing transformation.

Run the following command to install this library.

pip install ‘ray[data]’

Ray Train-

Designed for distributed model training, this library enables you to train your machine-learning models across multiple nodes, improving efficiency and speed. Best used for model training.

Ray Train

Image Credits: Projectpro

Run the following command to install this library.

pip install ‘ray[train]’

Ray RLlib–

Specifically built for reinforcement learning workloads, this library provides tools and algorithms to develop and train RL models.

Ray Tune–

If you’re looking to optimize your model’s performance, Ray Tune is the library for efficient hyperparameter tuning. It helps you find the best combination of parameters to enhance your model’s accuracy.

Ray tune can parallelize and leverage multiple cores of GPU and multiple CPU cores. It optimizes the hyperparameter tuning cost by providing optimization algorithms. Best used for Model hyperparameter tuning.

Run the following command to install this library.

pip install ‘ray[tune]’

Ray Serve–

Once your models are trained, Ray Serve comes into play. It allows you to easily serve your models, making them accessible for predictions or other applications.

Run the following command to install this library.

pip install ‘ray[serve]’

Ray benefits Data Engineers and Scientists

Ray has made it easier for data scientists and machine learning practitioners to scale apps without having in-depth knowledge of infrastructure. It helps them in

  • Parallelizing and distributing workloads- You can efficiently distribute your tasks across multiple nodes and GPUs, maximizing the utilization of computational resources.
  • Easy access to cloud computing resources- Ray simplifies the configuration and utilization of cloud-based computing power, ensuring quick and convenient access.
  • Native and extensible integrations- Ray seamlessly integrates with the machine learning ecosystem, providing you with a wide range of compatible tools and options for customization.

For distributed systems engineers, Ray handles critical processes automatically, including-

  • Orchestration- Ray manages the various components of a distributed system, ensuring they work together seamlessly.
  • Scheduling- It coordinates the execution of tasks, determining when and where they should be performed.
  • Fault tolerance- Ray ensures that tasks are completed successfully, even in the face of failures or errors.
  • Auto-scaling- It adjusts the allocation of resources based on dynamic demand, optimizing performance and efficiency.

In simple terms, Ray empowers data scientists and machine learning practitioners to scale their work without needing deep infrastructure knowledge, while offering distributed systems engineers automated management of crucial processes.

The Ray Ecosystem

The Ray Ecosystem

Image Credits: Thenewstack

Ray’s universal framework acts as a bridge between the hardware you use (such as your laptop or a cloud service provider) and the programming libraries commonly used by data scientists. These libraries can include popular ones like PyTorch, Dask, Transformers (HuggingFace), XGBoost, or even Ray’s own built-in libraries like Ray Serve and Ray Tune.

Ray occupies a distinct position that addresses multiple problem areas.

The first problem Ray tackles is scaling Python code by efficiently managing resources such as servers, threads, or GPUs. It accomplishes this through essential components: a scheduler, distributed data storage, and an actor system. Ray’s scheduler is versatile and capable of handling not only traditional scalability challenges but also simple workflows. The actor system in Ray provides a straightforward method for managing a resilient distributed execution state. By combining these features, Ray operates as a responsive system, where its various components can adapt and respond to the surrounding environment.

Reasons Top Companies Are Looking For Python Ray

Below are significant reasons why companies working on ML platforms are using Ray.

A powerful tool supporting Distributed Computing Efficiently

With Ray, developers can easily define their app’s logic in Python. Ray’s flexibility lies in its support for both stateless computations (Tasks) and stateful computations (Actors). A shared Object Store simplifies inter-node communication.

You may like to know: Ruby Vs Python: Which One to Embrace in 2024?

This allows Ray to implement distributed patterns that are way beyond the concept of simple data parallelism, which involves running the same function on different parts of a dataset simultaneously. In case of the machine learning applications, Ray supports more complex patterns.

Reasons Top Companies Are Looking For Python Ray

Image Credits: Anyscale

These capabilities allow developers to tackle a wide range of distributed computing challenges in machine learning applications using Ray.

An example that demonstrates the flexibility of Ray is the project called Alpa, developed by researchers from Google, AWS, UC Berkeley, Duke, and CMU for simplifying large deep-learning model training.

Sometimes a large model cannot fit on the same device like a GPU, this type of scaling requires partitioning a computation graph across multiple devices distributed on different servers. These devices perform different types of computations. This parallelism involves two types: inter-operator parallelism (assigning different operators to different devices) and intra-operator parallelism (splitting the same operator across multiple devices).

Python Ray Computational Graph

Image Credits: Anyscale

Alpa brings together different ways of doing multiple tasks at once by figuring out and doing the best ways to split up and do things both within and between steps. It does this automatically for really big deep-learning models that need lots of computing power.

To make all this work smoothly, the creators of Alpa picked Ray as the tool for spreading out the work across many computers. They went with Ray because of its capability to handle different ways of doing things at once and make sure the right tasks are done on the right computers. Ray is the perfect fit for Alpa because it helps it run big and complex deep-learning models efficiently and effectively across many computers.

Few lines of code for complex deployments

Ray Serve, also known as “Serve,” is a library designed to enable scalable model inference. It facilitates complex deployment scenarios including deploying multiple models simultaneously. This capability is becoming increasingly crucial as machine learning models are integrated into different apps and systems.

With Ray Serve, you can orchestrate multiple Ray actors, each responsible for providing inference for different models. It offers support for both batch inference, where predictions are made for multiple inputs at once, and online inference, where predictions are made in real time.

Ray Serve is capable of scaling to handle thousands of models in production, making it a reliable solution for large-scale inference deployments. It simplifies the process of deploying and managing models, allowing organizations to efficiently serve predictions for a wide range of applications and systems.

Efficiently scaling Diverse Workload

Ray’s scalability is a notable characteristic that brings significant benefits to organizations. A prime example is Instacart, which leverages Ray to drive its ML pipeline for large-scale completion. Ray empowers Instacart’s ML modelers by providing a user-friendly, efficient, and productive environment to harness the capabilities of expansive clusters.

With Ray, Instacart’s modelers can tap into the immense computational resources offered by large clusters effortlessly. Ray considers the entire cluster as a single pool of resources and handles the optimal mapping of computing tasks and actors to this pool. As a result, Ray effectively removes non-scalable elements from the system, such as rigidly partitioned task queues prevalent in Instacart’s legacy architecture.

By utilizing Ray, Instacart’s modelers can focus on running models on extensive datasets without needing to dive into the intricate details of managing computations across numerous machines. Ray simplifies the process, enabling them to scale their ML workflows seamlessly while handling the complexities behind the scenes.

Another biggest example is OpenAI.

Scaling Complex Computations

Ray is not only useful for distributed training, but it also appeals to users because it can handle various types of computations that are important for machine learning applications.

  • Graph Computations: Ray has proven to be effective in large-scale graph computations. Companies like Bytedance and Ant Group have used Ray for projects involving knowledge graphs in different industries.
  • Reinforcement Learning: Ray is widely used for reinforcement learning tasks in various domains such as recommender systems, industrial applications, and gaming, among others.
  • Processing New Data Types: Ray is utilized by several companies to create customized tools for processing and managing new types of data, including images, video, and text. While existing data processing tools mostly focus on structured or semi-structured data, there is an increasing need for efficient solutions to handle unstructured data like text, images, video, and audio.

Supporting Heterogeneous Hardware

As machine learning (ML) and data processing tasks continue to grow rapidly, and the advancements in computer hardware are slowing down, hardware manufacturers are introducing more specialized hardware accelerators. This means that when we want to scale up our workloads, we need to develop distributed applications that can work with different types of hardware.

One of the great features of Ray is its ability to seamlessly support different hardware types. Developers can specify the hardware requirements for each task or actor they create. For example, they can say that one task needs 1 CPU, while an actor needs 2 CPUs and 1 Nvidia A100 GPU, all within the same application.

Uber provides an example of how this works in practice. They improved their deep learning pipeline’s performance by 50% by using a combination of 8 GPU nodes and 9 CPU nodes with various hardware configurations, compared to their previous setup that used 16 GPU nodes. This not only made their pipeline more efficient but also resulted in significant cost savings.

Supporting Heterogeneous Hardware

Image Credits: Anyscale

Use Cases of Ray

Below is the list of popular use cases of Ray for scaling machine learning. 

Batch Interface

Batch inference involves making predictions with a machine learning model on a large amount of input data all at once. Ray for batch inference is compatible with any cloud provider and machine learning framework. It is designed to be fast and cost-effective for modern deep-learning applications. Whether you are using a single machine or a large cluster, Ray can scale your batch inference tasks with minimal code modifications. Ray is a Python-centric framework, making it simple to express and interactively develop your inference workloads.

Many Model Training

In machine learning scenarios like time series forecasting, it is often necessary to train multiple models on different subsets of the dataset. This approach is called “many model training.” Instead of training a single model on the entire dataset, many models are trained on smaller batches of data that correspond to different locations, products, or other factors.

When each individual model can fit on a single GPU, Ray can handle the training process efficiently. It assigns each training run to a separate task in Ray. This means that all the available workers can be utilized to run independent training sessions simultaneously, rather than having one worker process the jobs sequentially. This parallel approach helps to speed up the training process and make the most of the available computing resources.

Below is the data parallelism pattern for distributed training on large and complex datasets.

Many Model Training

Image Credits: Ray

Model Serving 

Ray Serve is a great tool for combining multiple machine-learning models and business logic to create a sophisticated inference service. You can use Python code to build this service, which makes it flexible and easy to work with.

Ray Serve supports advanced deployment patterns where you need to coordinate multiple Ray actors. These actors are responsible for performing inference on different models. Whether you need to handle batch processing or real-time inference, Ray Serve has got you covered. It is designed to handle large-scale production environments with thousands of models.

In simpler terms, Ray Serve allows you to create a powerful service that combines multiple machine-learning models and other code in Python. It can handle various types of inference tasks, and you can scale it to handle a large number of models in a production environment.

Hyperparameter Tuning 

The Ray Tune library allows you to apply hyperparameter tuning algorithms to any parallel workload in Ray.

Hyperparameter tuning often involves running multiple experiments, and each experiment can be treated as an independent task. This makes it a suitable scenario for distributed computing. Ray Tune simplifies the process of distributing the optimization of hyperparameters across multiple resources. It provides useful features like saving the best results, optimizing the scheduling of experiments, and specifying different search patterns.

In simpler terms, Ray Tune helps you optimize the parameters of your machine-learning models by running multiple experiments in parallel. It takes care of distributing the workload efficiently and offers helpful features like saving the best results and managing the experiment schedule.

Distributed Training

The Ray Train library brings together various distributed training frameworks into a unified Trainer API, making it easier to manage and coordinate distributed training.

When it comes to training many models, a technique called model parallelism is used. It involves dividing a large model into smaller parts and training them on different machines simultaneously. Ray Train simplifies this process by providing convenient tools for distributing these model shards across multiple machines and running the training process in parallel.

Reinforcement Learning

RLlib is a free and open-source library designed for reinforcement learning (RL). It is specifically built to handle large-scale RL workloads in production environments. RLlib provides a unified and straightforward interface that can be used across a wide range of industries.

Many leading companies in various fields, such as climate control, industrial control, manufacturing and logistics, finance, gaming, automobile, robotics, boat design, and more, rely on RLlib for their RL applications. RLlib’s versatility makes it a popular choice for implementing RL algorithms in different domains.

In simpler terms, the Ray Train library makes it simple to manage distributed training by combining different frameworks into one easy-to-use interface. It also supports training multiple models at once by dividing the models into smaller parts and training them simultaneously on different machines.

Experience Blazing-fast Python Distributed Computing with Ray 

Ray’s powerful capabilities in distributed computing and parallelization revolutionize the way applications are built. With Ray, you can leverage the speed and scalability of distributed computing to develop high-performance Python applications with ease. 

OnGraph, a leading technology company, brings its expertise and dedication to help you make the most of Ray’s potential. OnGraph enables you to develop cutting-edge applications that deliver unparalleled performance and user experiences. 

With OnGraph, you can confidently embark on a journey toward creating transformative applications that shape the future of technology.

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Top 5 Tips to Improve Survey Quality

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Are your survey Programming not effective enough to drive accurate and reliable information? Beat complex survey challenges with our best survey designing tips to improve survey quality and get better customer insights.

Discovering your audience’s preferences throughout their customer journey can be effectively accomplished through surveys. These valuable tools enable you to swiftly gather feedback from a large number of individuals within a short span. The logistics involved are comparatively straightforward- you design and deploy the survey, respondents participate at their convenience, and you subsequently analyze the collected data.

Surveys provide a wealth of information concerning your audience, encompassing various aspects such as.

 

  • Purchasing process- What information do customers require to fulfil their needs? Where do they seek such information? What kind of details aid them in making purchasing decisions?
  • Website satisfaction- Why do customers visit your company’s website? Can they easily access the desired information? Are there additional resources or tools you can offer them?
  • Post-event feedback- What is your customers’ perception of your company’s trade show booth? What aspects did they find most valuable? Is there specific information they were seeking but did not find?

Surveys are crucial for gathering information, hence it’s worth investing our time and effort to make them more engaging and innovative to support their purpose. So businesses must not overlook poor-quality surveys and seek the help of experts to improve survey quality.

Why Business Must Improve Survey Quality?

Improving survey quality can bring numerous benefits across various domains. Here are some key advantages.

 

  • Reliable insights

Improving survey quality ensures that the insights obtained are representative and unbiased. It helps minimize errors, sampling biases, and methodological flaws that can distort the results. Reliable insights enable organizations to make informed decisions, formulate effective strategies, and address specific challenges with greater precision.

 

  • Enhanced credibility

High-quality surveys enhance the credibility and reputation of the organizations conducting them. When surveys are designed and implemented with rigor and transparency, stakeholders perceive the organization as trustworthy and competent. This credibility can foster stronger relationships with customers, clients, employees, and other key stakeholders.

 

  • Improved customer satisfaction

By conducting surveys with better quality, organizations can gain a deeper understanding of customer needs, preferences, and satisfaction levels. This information helps identify areas for improvement, develop more targeted marketing strategies, and deliver better products and services. Ultimately, improved customer satisfaction leads to increased loyalty, repeat business, and positive word-of-mouth.

These advantages can positively impact decision-making, organizational performance, and the overall quality of products, services, and interventions.

Below are the best and proven tips to improve survey quality within no time.

5 Proven Tips to Improve Survey Quality

Tip 1- Goal-specific Survey Improves Survey Quality

When creating surveys, it is crucial to have a clear purpose in mind. Whether you are seeking feedback on your customers’ experience or aiming to identify the main challenges faced by your target audience, every survey should serve a specific goal. Unfortunately, many companies make the mistake of sending out lengthy and vague surveys that cover a wide range of topics, from website experience to shipping prices. This approach is likely to annoy your customers.

To obtain meaningful answers to your questions, it is advisable to divide your surveys into shorter questionnaires, each focusing on a single objective. By doing so, you can gather the necessary data more efficiently and simplify the process of developing questions.

Maintaining a clear focus on your goals can also improve other aspects of survey design, such as avoiding ambiguous and overly complex questions. By ensuring your survey content is engaging and relevant to your customers, you increase their willingness to participate. For further insights and statistical references, you can refer to the related resources.

Clear survey purpose enables-

  • Appropriate question selection
  • Elimination of ambiguity
  • Focus on goal-oriented questions
  • Inclusion of highly relevant questions

Tip 2- Say No to Jargon

Different respondents and customers attempt surveys from novice to experienced persons. If you do not make sure that your language suits every person out there, you might lose your valuable customers and their thoughts. It is better that you speak everyone’s language. Connect with your audience by using their preferred language.

When crafting your questions, it’s important to tailor them to your specific target audience. Choose wording and terminology that your respondents will easily comprehend. Steer clear of using internal jargon that may confuse them. If you do use abbreviations, make sure to provide a clear explanation of their meaning. By ensuring your questions are easily understood, you enable your respondents to provide more accurate and meaningful responses.

For example-

In the below example, using “AR” might confuse many people as they might have different understandings. They will make assumptions that will impact their answers, resulting in vague results.

In the second part, where we mentioned “AR” as “ Augmented Reality” will clarify the scenario and people will be able to answer correctly, resulting in accurate results. 

In most scenarios, it is better that you describe it clearly so that even the novice can answer in a better way.

improve survey quality with clear concepts

Image Credits: Pollfish

Tip 3- Is your Survey too Lengthy? Trim it now!

Customers do not want to spend much time completing surveys. They get bored easily if they have to attend lengthy surveys. Result? Quitting the survey in the middle.

This might severely damage your results. To maximize respondent engagement, it is crucial to ensure that your survey is concise. It is important to have a clear understanding of the specific information you wish to gather.

According to Cameron Johnson, business segment leader at Nextiva, research suggests that surveys that can be completed within five minutes or less tend to yield the highest response rates. Conversely, surveys that exceed 11 minutes in length are deemed too long and are likely to result in higher rates of abandonment or, even worse, random responses.

In essence, if your survey is excessively long, individuals are likely to expedite their responses in order to complete it quickly. They may not thoroughly contemplate their answers and could potentially misinterpret the questions, thus negatively impacting the quality of your data. By keeping your survey concise, you can achieve improved outcomes and foster greater engagement from your audience.

Tip 4- Curate Survey Questions Smartly

Are your survey questions making sense? Making sense at all? Or making dual sense that might confuse customers?

Then you must work on your words and curate questions that present a clear picture of what exactly you are looking for. The choice of words can greatly impact the quality of responses and prevent biased or confusing answers. Below are some valuable tips for creating effective survey questions.

  • Use unbiased questions that avoid leading respondents to a particular answer. For instance, instead of asking, “When do you enjoy using this product?” which introduces bias, ask, “When was the last time you used this product?”

improve survey quality with biased/unbiased questions

Image Credits: Forms.app

  • Pose one question at a time to avoid confusion and obtain clearer results. Often, surveys bundle multiple questions together, leading to muddled responses. For example, rather than combining “Do you prefer this product? If so, why?” into a single question, separate them. First, inquire about product usage and then ask for the reasons behind the preference.

improve survey quality with single question

Image Credits: Pollfish

  • Offer balanced response options to ensure unbiased results. Use answer scales that include an equal number of positive and negative choices, with a neutral option in between. A well-balanced scale could include options like Excellent, Good, Average, Below Average, and Poor.

improve survey quality with balanced options

Image Credits: dataforceresearch

  • If you’re uncertain about the clarity of your questions, enlist a test group to take the survey. Seek feedback from colleagues or friends to identify any potential confusion and make necessary adjustments.

Tip 5- Adding Question Variety to Improve Survey Quality

When it comes to gathering information, active engagement is the key. By incorporating a variety of question types, such as ranking, matrix, open-ended, or multiple-choice questions, you can captivate your audience and maintain their interest, even in a lengthier survey.

This requires a clear understanding of different types of survey questions and how can you use them to support your results, ensuring survey quality.

  • Open-ended or Close-ended questions

improved survey quality with the right question

Image Credits: embedsocial

  • Multiple-choice questions

improve survey quality with multiple choices

Image Credits: embedsocial

  • Dichotomous questions

improve survey quality with yes-no-questions

Image Credits: embedsocial

  • Rating-scale questions

improve survey quality with rating-scale-question

Image Credits: embedsocial

Such questions will keep the boredom away and help customers to engage throughout surveys irrespective of the length but do not exaggerate the survey length. Leverage the right survey creation tool that will turn your creative ideas into reality and improve survey quality.

Improve Survey Quality with OnGraph’s Customized Survey Creation Tool

Unleash the power of surveys to understand your audience! Customize questions to match research objectives and gather vital information. Write in a language that resonates with your audience. Simplify survey participation with diverse question types, logical flow, and skip logic. Seek a fresh perspective from a colleague to spot any confusion. 

Elevate your insights and connect with your audience like never before with the right customized Survey Creation Tool at your fingertips. We have experts who can custom-build tools to turn your survey expectations into reality for better and more accurate results. That’s why businesses must not overlook survey quality for any reason.

Apart from curating amazing surveys, protect them against fraud. Read our blog- Safeguard Market Research: The Ultimate Guide to Fraud Detection.

Drop us a query today, we will be happy to help and improve your survey quality.

If you have implemented other tips and tricks or something better in mind to improve survey quality, let us know via comments. We will be glad to hear.

 

 

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10+ Python Libraries for Data Science and Machine Learning

Python Libraries

In today’s fast-paced digital era, Data Science and Machine Learning have emerged as the most sought-after technologies. The demand for skilled professionals in these domains has skyrocketed, urging individuals to upskill themselves with various Python libraries to effectively implement these cutting-edge technologies.

If you’re looking to stay ahead in the game and master these two fast-growing skills in the market, then you’ve come to the right place. Whether you’re a beginner or an experienced professional, you must get along with Python libraries to be in the competitive landscape. So, fasten your seatbelts and upskill your game!

In this blog, we will help you understand how Python can be a game-changer for ML and DS, and what libraries help to ease the progress. We have listed the Best Python Libraries for Machine Learning and Data Science.

Before that, we will take a quick understanding of Machine learning and Data Science. 

A quick peek into Data Science and Machine Learning

As I delved into the world of Data Science and Machine Learning, I couldn’t help but wonder what all the fuss was about. But the reason was in front of all, the abundance of data we produce every day. With so much information at our fingertips, Data Science has become the go-to field for extracting valuable insights and solving real-world problems. 

But let’s not forget that both Data Science and Machine Learning are more than just technologies – they’re skills that require expertise in analyzing data and developing predictive models. 

At the core, Data Science is all about extracting valuable and resourceful insights from data, while Machine Learning involves teaching machines to solve modern-age challenges by processing vast amounts of data. Thus, boosting the demand for data scientists and machine learning professionals globally.

These two fields are closely linked, with Machine Learning algorithms and statistical techniques being an essential domain of Data Science. But, how can one create an optimized model to do all the work? 

Well, different programming languages are there such as Python, R, Java, and others help to ease the python app development process. Among them, Python is the most widely used language due to its versatility and extensive libraries. As per ResearchGate, Python is the preferred language for Data Science and Machine Learning. 

A quick peek into Data Science and Machine Learning

But where does Python come into play for machine learning and data science? Let’s explore the reasons.

Why learn Python Libraries for Machine Learning and Data Science?

Python has taken the tech world by storm! When it comes to implementing Machine Learning and Data Science, it oversees the other programming languages. Python dominates in Machine Learning and Data Science due to its versatility, ease of use, extensive libraries, and unparalleled popularity among engineers and data scientists.

So, if you’re looking to dive into the world of Machine Learning and Data Science, it’s time to add Python to your skill set!

Why learn Python Libraries for Machine Learning and Data Science?

Easy to learn:

Python’s simplicity makes it a versatile language, capable of handling simple tasks like concatenating strings as well as complex ones like creating intricate ML models.

Less coding:

Data Science and Machine Learning require numerous algorithms, but with Python’s pre-built packages, there’s no need to code from scratch. Plus, Python’s “check while you code” approach makes testing easier, taking the burden off developers.

Platform-independent:

Python is a versatile programming language compatible with different platforms, such as Windows, macOS, Linux, and Unix. Moving code between platforms can be tricky due to differences in dependencies, but tools like PyInstaller can simplify the process by managing these issues for you. So you can focus on writing your code and let the packages handle the rest.

Strong and active community support:

With so many people using Python for data science, it’s easy to find help and support when you need it.

Imagine having a question or facing a challenge while working on a data science project, and not having anyone to turn to for help. That’s a recipe for frustration and lost time. But with Python’s active community, you never have to feel alone in your data science journey.

The Python community warmly welcomes both novices and experts in the field of data science. There’s a wealth of resources available, from online forums and social media groups to local meetups and conferences, where you can interact with fellow enthusiasts and gain valuable insights from their experiences.

Prebuilt libraries:

Python offers an array of ready-to-use libraries to embrace the world of Machine Learning and Deep Learning. These powerful packages can be effortlessly installed and loaded with a single command, sparing you the hassle of starting from scratch. Among the popular pre-built libraries, you’ll find the likes of NumPy, Keras, TensorFlow, and PyTorch, just to scratch the surface. Get ready to unlock endless possibilities with Python’s arsenal of tools!

In a nutshell, Python libraries are ingenious tools that empower programmers and data enthusiasts to turn their ambitious ideas into reality with greater speed and finesse. For those who are not aware of its actual importance, then we have listed the significant benefits of Python libraries.

You may like to know: Ruby Vs Python: Which One to Embrace in 2024 | Pros and Cons

Significance of Python Libraries

Python is popular among developers due to the following significant advantages.

Code Reusability:

Python libraries provide pre-built functions and modules that can be reused across different projects, saving time and effort. Python Developers can leverage the existing codebase to accelerate development.

Increased Productivity:

Libraries offer high-level abstractions and simplified APIs, enabling developers to write code more efficiently. They eliminate the need to reinvent the wheel for common tasks, allowing developers to focus on solving specific problems.

Vast Functionality:

Python libraries cover a wide range of domains, from scientific computing and data analysis to web app development and machine learning. By utilizing libraries, developers gain access to extensive functionality and tools tailored for specific tasks. Some commonly used Python Libraries for Data Analysis and Visualization- TensorFlow, scikit-learn, and more.

Community Support:

Python has a large and active community of developers who contribute to libraries. This means you can find support, documentation, and examples readily available online. Community-driven libraries often receive updates and bug fixes, ensuring better reliability and compatibility.

Performance Optimization:

Many Python libraries are built on top of highly optimized lower-level languages, such as C or C++. They provide fast execution times for computationally intensive tasks, enabling efficient data processing and analysis.

Platform Independence:

Python libraries are designed to be platform-independent, making them suitable for various operating systems like Windows, macOS, and Linux. This cross-platform compatibility allows developers to write code that can run seamlessly on different environments.

Integration with Existing Systems:

Python libraries often offer integration capabilities with other technologies, python frameworks, and systems. This facilitates interoperability, allowing developers to combine Python with other languages and tools within their software stack.

Rapid Prototyping and Development:

Libraries provide ready-made app solutions and components, enabling quick prototyping and development of projects. They eliminate the need to start from scratch and speed up the iteration process.

Cost-Effective Development:

Leveraging existing libraries reduces development costs by reducing the need for custom code development. This is particularly beneficial for small teams or individuals with limited resources.

Python’s extensive library range benefits businesses in different ways and helps in creating a next-level experience for all. These libraries have contributed a lot to the field of machine learning and data science. If you belong to the data science and machine learning field then you must be aware of the following libraries to do it all.

Essential Python Libraries for Data Science and Machine Learning

Building ML models to accurately predict outcomes or solve problems is crucial in Data Science projects. It involves coding numerous lines of complex code, especially when dealing with complex problems. Well, this is where Python comes into play.

Python’s popularity in the DS and Machine Learning field is mainly attributed to its vast collections of built-in libraries. These libraries offer a plethora of ready-to-use functions that facilitate data analysis, modeling, and more. This makes it easy for developers to streamline their workflow and focus on building smarter and more efficient algorithms, handling complex algorithms, and computations. 

So, if you want to work on more advanced and complex problems, then you must be aware of these Popular Python Libraries for Machine Learning and Data Science that will ease your project work.

Python libraries for data science and machine learning

Let’s understand the core features of these Easy-to-use Python Libraries for Data Science and Beginner-friendly Python Libraries for Machine Learning.

NumPy:

NumPy is a popular and must-have Python Libraries for Data Science Projects and scientific computing. It’s loved for its ability to handle multi-dimensional arrays and complex operations. With NumPy, you can easily manipulate images as arrays of real numbers, and even sort and reshape data. It’s a must-have for any Python developer working in the fields of data science or machine learning.

Key Features-

  • Can perform complex computations
  • Data manipulation is made easier with routines and Fourier transformations.
  • Makes it seamless to carry out Linear algebra operations, such as Linear Naive Bayes, Regression, etc.

SciPy:

The SciPy library, a collection of powerful tools for statistical analysis, is like a superhero cape for NumPy. Together, they tackle complex math problems and process arrays like nobody’s business. While NumPy sets the foundation, SciPy swoops in with specialized sub-packages to solve even the toughest equations. It’s like having a trusty sidekick to help you save the day!

Key Features-

  • Works with NumPy arrays
  • Offers various mathematical methods (numerical integration, optimization)
  • Contains sub-packages for Fourier transformation, interpolation, integrations, etc.
  • Includes functions for Linear Algebra for advanced computations.
  • Enables the creation of sparse matrices

Pandas:

Pandas, a vital statistical library, find applications in diverse fields like finance, economics, and data analysis. It uses NumPy arrays to process data objects and collaborates closely with NumPy and SciPy is Python Libraries for Data Manipulation and Cleaning. Pandas are great for handling large data sets.

Key Features-

  • Efficiently generates DataFrame objects using predefined and customizable indexing
  • Enables manipulation of vast datasets with ease, including Subsetting, Slicing, and Indexing
  • Built-in features for generating Excel sheets and doing data analysis tasks like, statistical analysis, visualization, etc.
  • You can easily alter the Time Series data.

You may like to know: Python Ray- Transforming Distributed Computing

Matplotlib:

Are you looking to make sense of your data? Look no further than Matplotlib – the go-to data visualization package for Python. With a plethora of graph options to choose from, including bar charts, and error charts, you can quickly transform your data into precise visuals. Matplotlib’s 2D graphical library is a must-have tool for any data analyst conducting Exploratory Data Analysis (EDA). 

Key Features-

  • Matplotlib facilitates easy plotting of graphs with appropriate styles and formatting.
  • The graphs help understand trends, and patterns and make correlations with quantitative data.
  • pyplot module offers a MATLAB-like interface for plotting graphs.
  • It has an API module to incorporate graphs into GUI applications like Tkinter, and Qt.

TensorFlow:

Looking for a powerful tool to master Deep Learning? Then TensorFlow is your way to go. It is an open-source Python library curated for dataflow programming. With its symbolic math capabilities, you can easily build precise and robust neural networks. Plus, its user-friendly interface is highly scalable and perfect for a broad range of fields. 

Key Features-

  • Lets you build and train multiple neural networks
  • Works well for large-scale projects and data sets
  • Provides support for Neural Networks
  • Performs statistical analysis
  • Probabilistic models and Bayesian Networks can be created using built-in functions.
  • Layered components are used to perform operations on weights and biases.
  • Regularization techniques such as batch normalization, dropout, etc. can be implemented.
  • TensorBoard, a visualizer, is included.
  • Interactive graphs and visuals are created.
  • Helps in understanding data feature dependencies.

Scikit-Learn:

Scikit-learn is a must-have Python library for creating and evaluating data models. Packed with an abundance of functions, it supports both Supervised and Unsupervised ML algorithms, and Boosting functions. It’s the ultimate tool for anyone seeking top-notch performance and accuracy in data modeling.

Key Features-

  • In-built methods for both (Supervised and Unsupervised) ML operations, such as classification, regression, and detecting anomalies.
  • Cross-validation methods for model performance estimation.
  • Offer parameter tuning functions to improve model performance.

PyTorch:

It is a powerful open-source tool that uses Python to apply cutting-edge Deep Learning techniques and Neural Networks to vast amounts of data. It’s a go-to choice for Facebook in developing neural networks for tasks like recognizing faces and tagging photos automatically. With PyTorch, researchers and developers have a flexible and efficient framework to bring their AI projects to life.

Key Features-

  • Seamless integration with data science and ML frameworks through user-friendly APIs
  • PyTorch supports multi-dimensional arrays called Tensors
  • Utilization of GPU for faster computation using Tensors in PyTorch
  • Over 200 mathematical operations available in PyTorch for statistical analysis
  • Dynamic Computation Graphs for time series analysis and real-time sales forecasting.

spaCy:

spaCy is a free, open-source library in Python used for advanced Natural Language Processing (NLP) tasks, developed and maintained by Explosion AI. It is appreciated for its simplicity, efficiency, and integration with deep learning frameworks. Not only does it offer pre-trained statistical models and word vectors, but it also supports more than 60 languages. It’s designed for production use, enabling efficient processing of large text volumes due to its optimized implementation in Python. 

Key features- 

  • Tokenization
  • Named Entity Recognition (NER)
  • Part-of-speech (POS) tagging
  • dependency parsing
  • Lemmatization
  • Sentence Boundary Detection (SBD)
  • Text classification
  • Entity linking, similarity comparisons, custom pipeline components, and support for word vectors and multi-language.

Apache Spark:

Apache Spark is an open-source, distributed computing system used for big data processing and analytics. Developed by the Apache Software Foundation, Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It was created to address the limitations of Hadoop MapReduce, offering improvements in speed, ease of use, and flexibility.

Key features-

  • High-speed performance, due to in-memory processing capabilities, allows up to 100 times faster processing in memory and 10 times faster on disk than disk-based engines.
  • Ease of use with high-level APIs in Java, Scala, Python, and R, plus an interactive shell in Scala and Python.
  • Libraries for various data analysis tasks such as Spark SQL for structured data processing, MLlib for machine learning, GraphX for graph processing, and Streaming for stream processing.
  • Ability to run on various platforms (Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud) and access diverse data sources (HDFS, Apache Cassandra, Apache HBase, Amazon S3).
  • Resilient Distributed Datasets (RDDs) are immutable distributed object collections that can be processed in parallel.
  • DataFrames and Datasets, which are abstractions seen as distributed tables in Spark and support operations like selection, filtering, and aggregation.
  • Fault tolerance is achieved through the RDD and DataFrame/Dataset abstractions, which can recompute missing or damaged partitions in case of node failures.
  • Real-time computation capacity through Spark Streaming, enabling scalable, high-throughput, fault-tolerant stream processing of live data streams.

Hugging Face:

Hugging Face is a company known for its work in Natural Language Processing (NLP) and Artificial Intelligence (AI). They provide a platform for training and deploying AI models, and are especially noted for their transformers library, which includes pre-trained versions of many state-of-the-art models in NLP.

Their popular Transformers library is built with a focus on two things: interoperability and user-friendliness. Interoperability is achieved by providing consistent training and serving interfaces for different transformer models. This means that users can easily switch between different models with minimal changes in their code.

The library currently includes pre-trained models for tasks like text classification, information extraction, summarization, translation, and more. It also provides various tokenizers compatible with the included models. Some of the many models included are BERT, GPT-2, GPT-3 (though limited due to OpenAI’s API), RoBERTa, XLM, DistilBERT, and others.

The Hugging Face model hub is a place where trained models can be uploaded, downloaded, and shared globally. It includes thousands of pre-trained models contributed by the wider community. These models support over 100 languages and can be fine-tuned to suit particular tasks.

Hugging Face also maintains the Tokenizers library, which provides fast, efficient, and powerful tokenizers for various types of input data, and the Datasets library, a lightweight library providing easy-to-use access to a wide range of NLP datasets.

LangChain:

LangChain is a library that assists developers in integrating large language models (LLMs) into their applications. It provides a way to link these models with various data sources like the internet or personal files, enabling more complicated applications. 

The value of LangChain lies in its simplification of the process to implement LLMs, which can be complex, and its ability to link these models with diverse data sources. This expands the scope of information accessible to the models, enhancing the potential functionality and versatility of the applications built with them.

Key features-

  • LangChain offers adaptability, allowing easy customization and changes to components based on specific requirements.
  • The developers of LangChain continually strive to enhance its speed, ensuring access to the latest features from Large Language Models (LLMs).
  • LangChain boasts a robust and engaged community, providing ample support for those who need it.
  • While LLMs can handle simple tasks with ease, developing complex applications can present challenges. LangChain assists in overcoming these by offering features that simplify the creation of intricate applications using LLMs.

Keras:

If you’re looking to build top-notch deep learning models in Python, Keras is a must-have library. It’s got everything you need to create, analyze, and enhance your neural networks. And thanks to its integration with Theano and TensorFlow, Keras can handle even the most complex and expansive models with ease. To take your deep learning game to the next level, try Keras!

Key Features-

  • Enables the creation of diverse Neural Network types (FC, CNN, RNN, Embedding)
  • Facilitates model combination for big datasets and tasks
  • Built-in functionality for layer definition, optimization, activation, and objectives
  • Simplifies image and text data handling
  • Offers pre-processed datasets and pre-trained models (MNIST, VGG, Inception, SqueezeNet, ResNet)
  • Easily extensible and allows adding new modules including functions and methods.

Building complex applications and handling a pool of data with improved security and integrity, Python libraries have it all. 

The Future of Python for DS and ML

Python has become a darling among data scientists and is steadily gaining popularity with each passing day. With an increasing number of data scientists joining the industry, it’s safe to say that Python will continue to reign supreme in the data science world. And the best part is that as we make progress in machine learning, deep learning, and other data science tasks, we’ll have access to cutting-edge libraries that are available in Python.

Python has been around for years and has been well-maintained, which is evident from its continuous growth in popularity. Many companies have adopted Python as their go-to language for data science, which is a testament to its effectiveness. 

If you’re a seasoned data scientist or just starting on your data science journey, Python is the language you need to learn. Its simplicity and readability, combined with its supportive community and wide-ranging popularity, make it stand out from other programming languages. And with the abundance of libraries available for data cleaning, visualization, and machine learning, Python can streamline your data science workflow as no other language can. 

So if are looking for potential development solutions using Python, then you must consider an expert hand to do it for you. At OnGraph, we provide that expertise with 15+ years in Python development.

You may like to know: Python 3.12: Features and Improvements

So, if you want to work on more advanced and complex problems, then you must be aware of these Popular Python Libraries for Machine Learning and Data Science that will ease your project work.

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Demystifying Market Research Challenges

market research

Market research is the key to capturing your customers’ hearts, so it’s crucial to do it right by addressing core market research challenges with simple solutions.

We all know that market research could be overwhelming in today’s landscape where we have millions of people to target. Right from delivering rightful and faster insights to developing strategies, the market research and researcher roles have evolved.

With evolution, the challenges of getting accurate and reliable information are also increasing. Conducting precise market research might not bring the results that can transform your sales.

Then where is your research going wrong? What challenges are you facing and how can you deal with those? Worry not because you are in the right place. Getting more insights can be challenging, so you must consider outsourcing a market research development company. 

In this blog, we will shed light on the core challenges of market research and provide simple market research software solutions to help you overcome them effortlessly.

To know about what Market Research is, Read our blog- The Fundamentals of Market Research.

Potential Market Research Challenges with Tech-led Solutions

Below are potential challenges that might impact your business decisions. 

Challenge 1- Market Uncertainty leads to inaccurate Business Decisions

Market uncertainty simply means that you are not certain about the targeted market or do not have accurate information about the latest trends in the market.

With the pandemic hit, this uncertainty reaches its peak, all supply chains were disrupted, and business workflow had gone under a drastic change, creating chaos, and leaving the market with great uncertainty.

With time this challenge will keep on increasing at an alarming rate. The probable reason is the consistent change in customer behavior, changing market trends, increase in global data to consider, and increase in the number of customers spending time online. Such factors might not be in favor of gathering accurate information.

One of the most known examples of market uncertainty is Brexit, causing national income to fall by 3.6 percent within 2 years. This uncertainty disrupted the market and businesses were not able to make informed decisions.

Another major business failure due to market uncertainty was Nokia. Nokia failed to keep up with the market’s newest trends of the time while competitors took leverage. Back in 2007, Nokia was the biggest phone seller in the world, while Apple was just starting. But within six years, Nokia’s value dropped by 90%, while Apple became a global leader.

So how could you come over with market uncertainty?

Solution 1- Leveraging AI solutions and tools

To control this uncertainty businesses need tools that help businesses in getting real-time data and updates of the market to understand the outcomes better. In simple words, businesses should be able to extract meaningful and accurate insights without being impacted by market uncertainty. 

Leveraging modern-age technologies like AI (Artificial Intelligence) can help businesses get real-time data and many more benefits.

AI in market research

Today, businesses can find several OpenAI-powered tools to make market research much better and faster.

OpenAI-powered tools to make market research

Solution 2- Automation Crucial Tasks

Keep an eye on the processes and tasks that are time-consuming that can often lead to human errors. If possible, automating those tasks can help a lot and provide more accurate results. 

So, market research can also benefit from automation. One way is by using robots to test how effective advertising is.

Companies want to know certain things about their advertising campaigns before they launch (like if the message and execution are good), while they are running (to see how the target audience is responding), and after they finish (to see the return on investment).

Automation makes this kind of research more accurate, faster, and cost-effective for companies. It also makes ad testing easier to understand and allows human researchers to spend more time analyzing the results and providing valuable insights that companies want.

Solution 3- Upskilling Your Workforce

It’s important to prepare your employees for using new tools like analytics, AI, and other advanced technologies. Begin training them now, so you can make the most of the technology once you have it. Instead of following the traditional approach of “build it and they will come,” take a more proactive and employee-centered approach.

As the leader of the company, you should establish the overall direction and goals for training. Provide your employees with the time, tools, and resources they need to learn and apply new skills. From there, let your employees take the lead in their upskilling by being innovative, building, sharing, and testing out solutions.

Challenge 2- Poor Quality Data leads to Vague Insights

Gathering information does not make any sense if it is useful for businesses in any aspect. You must ensure that the gathered information is accurate. People do not understand how poor-quality information can impact a business’s reputation. It can lead to poor customer relations, vague analytics, and poor decisions, impacting overall business performance.

Poor Quality Data in market research

As per a study, out of the 3.6 M leads, 45 percent are bad leads due to the duplicate data recorded, failed email verifications, and missing data. Resulting in missing potential business opportunities. The business’s failure to identify potential prospects’ low-quality data also weakens the business’s ability to curate proper product development strategy as per changing business needs. This gives competitive benefits to your competitors. 

Another potential damage that a business gets due to poor data quality.

So how do they get accurate insightful information and use it for decision making? 

To overcome such challenges, businesses must adopt the latest and technology-driven analytics tools that break the siloed processes. Below are some possible solutions to improve data quality.

Solution 1- Checking data quality with Fraud Detection Techniques

Businesses gather a lot of responses from global respondents who attend the surveys. But do they always provide the right information? Or how could you identify if they are human and not bots?

Thus it is necessary to quality-check your responses with powerful fraud detection techniques integration. Some techniques like- address verification, mobile verification, red herring questions, open/close-ended questions, GEO IP, fingerprinting, and other methods.

To know more about how can you keep your research safe, read Safeguard Market Research: The Ultimate Guide to Fraud Detection

Solution 2- Integrate trusted Survey Panels

Businesses cannot always find genuine respondents to attend the surveys. To ensure this, businesses can leverage trusted survey panels that have pre-screened respondents with complete pre-screening, ensuring relevant information for your research.

You can do it using our survey panels, gathering genuine responses worldwide.

Solution 3- Project Management Tools with proven statistical analytical tools

Another potential solution is the adoption of the proper platform that helps manage market research projects for better and high-quality insights. Combining qual and quantitative capabilities and datasets in a single platform helps businesses to find more holistic insights seamlessly. Having a platform integrated with modern tech can capture data from multiple sources and provide high-quality insights.

Also, businesses can include statistical analytics tools to get meaningful information.

Thus, businesses must have the right platform to manage projects and gather data.

We can help you develop customized and pre-built project management tools to elevate your research capabilities in improving the quality of data. 

Challenge 3- Getting Leaders to Invest

Today, conducting market research has become a staple for every business. Getting the right data from genuine sources, helps researchers get accurate insights, leaders can form their decisions, and product developers can develop solutions to business problems.

But what if the investor does not want to spend on your research project? Your project might fail due to the lack of investment you need.

As per a study, one of the significant reasons that startup fails are.

Leaders think about why to spend money on purpose if they do not see any outcome. What leads them to pull back the investment?

  • Proof of success is missing
  • Not having a solid business or research proposal
  • Not understanding the market
  • Lack of marketing strategy

This is the same thing that happened with Kodak: Ignoring Market Trends where the company failed to identify current trends among customers and anyway invested. This is why it’s hard to convince leaders to invest. 

So how can you eliminate these challenges? Let’s see.

Solution 1- Clarify the Research Outcome

Make sure to clarify the potential impact of conducting market research on business decisions and their outcome. For that, you need role-based dashboards to provide a high-level view of how various market research projects have impacted business outcomes.

With our Project management tool, you can pull reports and see real-time updates on each project.

Solution 2- Have a Better Understanding of Customers

You can also run product research by creating surveys to find out which features are likable to customers. You can gather insights and share them with the team to make necessary changes. 

After you have accurate information about what changes to implement, you can convince leaders to invest in making those changes and implement them across channels for better results and ROI.

Challenge 4- Not Targeting the Right Respondent

Trying to please everyone is one of the biggest mistakes a business can make. Most products often solve specific problems for specific groups of people. Everyone is not interested in Air Jordans, bacon-flavored soda, or organic soap made from peanut butter. That’s why it’s important to focus on the right target market to make the most of your advertising budget and attract customers naturally.

If you don’t understand what your target market wants, values, and prefers, it can lead to consequences. Analyzing the target market helps you learn about your customers, so you can choose the right advertising platforms, products, services, and messages that will encourage potential customers to take action.

According to the Zendesk Customer Experience Trends Report 2022, 68 percent of customers expect personalized experiences. If you do not understand your customer’s needs, you will not be able to create a personalized experience for them.

Not only this but targeting the wrong target can also impact your business credibility. This is what happens with Arch Deluxe: McDonald’s Market Research Disconnect where the brand overlooks the target market and ends up losing its investment.

Solution- Survey Panels

To overcome such challenges, businesses need a more effective approach that involves gathering information from diverse and validated perspectives. Only by doing so can we guarantee that the uncovered data comprehensively depicts the entire market.

They can integrate third-party suppliers via API or add manual trusted respondents to complete the surveys. We can help businesses with solutions like Survey panels with integrated supplies from genuine sources who can help you make informed decisions. 

Also for precise targeting of the market, you can set qualifications and quotas to find distinct sets of customers with different needs.

Challenge 5- Dependency on third parties to drive projects are costly

Most companies delegate complex research work to third parties due to the lack of skill, expertise, and knowledge of the marketing and market. Relying on third parties has become costly and limited. Businesses could not find more opportunities and find more sustainable ways to do the same research. 

Many market research companies find it challenging to invest that much in driving their projects. This extra cost can pose a huge challenge for startups. Costing is one of the top reasons why startups fail.

top reasons startup fails

Solution- DIY Platform

DIY (Do-It-Yourself) platforms allow you to conduct or field market research projects. These platforms provide tools and resources that enable individuals or businesses to design, execute, and analyze market research studies on their own.

To know how DIY platforms are transforming the market research industry, read our blog- Top Technology Trends for Market Research in 2023

More than 50 percent of companies that have utilized do-it-yourself (DIY) market research have witnessed exceptional financial growth. Furthermore, nearly half of market research experts are conducting a greater amount of DIY research now compared to six years ago.

Not only this, 70 percent of companies would like to transition to DIY tools for better results while keeping the budget in place.

Solve Market Research Challenges with OnGraph

OnGraph emerges as a reliable solution for overcoming the challenges faced by market research app software services companies. By addressing issues such as sample representativeness and the verification of data sources, OnGraph ensures that valuable insights are derived from a diverse range of validated viewpoints. 

With its commitment, OnGraph (market research app software services solutions) provides businesses with the necessary tools to make informed decisions and stay ahead in today’s dynamic marketplace.

If have some cross any major Market Research challenge we must highlight then drop us comments. Thanks for being an avid reader. 

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Gatsby: Building Amazing Web Experiences

Gatsby Feature

Get ready to witness the limitless capabilities of Gatsby, as Gatsby is here to revolutionize your web development experience.

To meet the expectations of modern-age UI, new frameworks have been introduced over time. From ensuring lightning-fast websites to better-ranking websites, we need advanced frameworks that ease development. 

One of the Javascript frameworks that get quick attention is the Gatsby: The fastest frontend for the headless web. With its seamless integration of React, Gatsby empowers you to build lightning-fast, visually stunning websites that leave a lasting impression on your audience. Say goodbye to sluggish load times and hello to a delightful user experience. 

Gatsby contributes mainly to a major portion of enterprises’ websites.

One who is looking to create an exceptional UI leveraging the power of React, then Gatsby is your way to go. If you are just getting started, then you must read this guide around Gatsby to fully understand how Gatsby can help your business grow.

Let’s start with a quick introduction to Gatsby.

What is Gatsby?

Gatsby.JS is an open-source platform, rooted in React, designed to assist developers in crafting high-speed web applications and websites. It assists coders in making websites and apps that work fast. By mixing the flexibility of dynamically changing sites with the quickness of static site creation, Gatsby lets developers explore many new possibilities on the web. 

It combines GraphQL, React, and react-router to make it easier for developers to build static sites. Think of Gatsby as a modern framework that optimizes page-loading speed using techniques like data prefetching and code splitting. It’s great for websites with a predictable number of pages and mostly static content. 

Gatsby provides many plugins, themes, and starters that developers can use to build websites in different ways. These plugins can modify almost everything in Gatsby. Gatsby JS has more starting themes and templates than any other framework, and it supports REST APIs, GraphQL, CMS systems, and databases.

Understanding Gatsby’s Buzzword

Gatsby is a modern web tool that utilizes new concepts like JAMstack architecture and static site generators to make websites load faster and run more smoothly. To understand Gatsby, we need to understand the technologies it relies on, which have been shaping web and web development in recent years.

Static site generators

A static site is constructed using static content (HTML, CSS, and JavaScript) and utilizes server-side rendering to deliver consistent content upon each site load. In contrast, dynamic websites employ browser-side rendering, resulting in varying page loads based on factors like time zone, location, and user behavior. 

Gatsby JS, powered by React, is a popular static site generator that uses GraphQL API to process data and create static HTML files.

The “JAMstack”

Jamstack uses JavaScript, reusable API, and pre-built Markup to make websites faster, more secure, and easier to handle.

 

JAMStack- Gatsby

Source- Zesty

Below are the major components of JAMstack-

  • JavaScript: It is used on the client side to add interactivity and dynamic functionality to websites.
  • APIs (Application Programming Interfaces): These are reusable services or functions that allow different parts of a website to communicate and exchange data with each other.
  • Markup: Pre-built Markup refers to static HTML files that are generated during the build process of a JAMstack website. These files are served to the client as-is, reducing the need for server-side processing.

 

Jamstack is a favored architecture for static site generators, offering a client-side web development approach. GatsbyJS sites can be hosted with Object Storage Service and CDN, eliminating the need for a web server. Overall, JAMstack combines these components to create a modern software architecture that improves website performance, security, and scalability.

Working of Gatsby

What makes Gatsby a prominent choice? Some features like code splitting and content mesh help developers use static files for building fast websites. The Gatsby’s build command splits the JavaScript code bundles allowing the loading of HTML, CSS, and JavaScript code rather than downloading the complete app. This is why Gatsby is fast.

Content mesh highlights how Gatbsy’s main features relate and work together.

 

Gatsy Working

Source- Hostinger

Gatsby retrieves resources from CMS or markdown files and stores them in separate folders. 

For example, each web page sourced from a CMS has its folder, so when a user enters a specific URL, Gatsby fetches and displays that page. After rendering it completely, Gatsby prefetches resources from other pages to speed up loading times. The Gatsby ecosystem includes numerous plugins for various functionalities like image optimization, lazy loading, social media integration, eCommerce, and analytics. When using Gatsby, it is important to choose a high-performance VPS web hosting that supports the framework.

Core Components of Gatsby

Gatsby concepts

Source- malcolmkee

When considering Gatsby for JAMstack site development, React plays a crucial role as the “J” in “JAM.” React components are extremely modular and reusable, making them well-suited for encapsulating dynamic content. Gatsby operates similarly to a standard React application, so if you’re already comfortable with React, you’ll find it easy to develop in Gatsby.

Now, let’s dig deeper into the core components of Gatsby. 

React in Gatsby

React is the “J” of “JAMstack”.

The role of a component in a Gatsby app relies on where it’s placed in the filesystem. Components in src/components serve as simple page components, generating pages with URLs based on their filenames. For example, src/pages/about.js would create mywebsite.com/about. Page template components, found in src/templates, are similar to page components but can query GraphQL to fetch markdown data. This data is then used to populate the generated page. Page template components are handy for creating resources on a site with a consistent layout but different content, like blog posts, user profiles, and dashboards.

GraphQL in Gatsby

GraphQL, similar to React, originated at Facebook and is recognized as a highly robust and versatile approach to fetching data into React components. Operating as a query language, GraphQL bears resemblance to SQL by allowing users to define the desired data in a “query” and receive it as a response.

Webpack in Gatsby

 

webpack Gatsby

Source- Hostinger

Webpack is a tool that combines JavaScript modules into optimized bundles, facilitating the execution of an application. It leverages the dependency graph to identify module interdependencies, ensuring proper functionality. The webpack.config.js file serves as a guide for determining entry points. Furthermore, these file stores configuration settings and enable the retrieval of markup files in Gatsby by embedding HTML and CSS. Additionally, webpack supports code-splitting, enabling the loading of specific bundles upon request.

Advantages of Gatsby

Below is the list of significant benefits of using Gatsby in your forthcoming development projects.

 

  • Speed and Performance

Gatsby generates static HTML files that can be served directly to users, resulting in fast loading times and improved performance. Once the initial page loads, subsequent navigation is almost instantaneous.

  • Scalability

Gatsby’s build process optimizes assets and only generates what’s necessary, making it scalable and capable of handling large websites efficiently.

  • SEO-Friendly

Gatsby’s static site structure and pre-rendered pages make it inherently search-engine friendly. It allows search engines to easily crawl and index your content, improving your site’s visibility.

  • React Ecosystem

Gatsby is built on React, which is a popular JavaScript library for building user interfaces. This means you can leverage the vast React ecosystem, including a wide range of plugins and components, to enhance your website’s functionality.

  • Content Management

Gatsby can integrate with various content management systems (CMS) like WordPress, Drupal, or Contentful. This allows you to create and manage content using familiar tools while benefiting from Gatsby’s performance optimizations.

  • Progressive Web App (PWA) Support

Gatsby supports building Progressive Web Apps, which are web applications that offer an app-like experience to users. PWAs can work offline, send push notifications, and provide an immersive user experience.

Disadvantages of Gatsby

Despite the benefits, there are some scenarios that you might feel disheartened with Gatsby.

 

  • Learning Curve

Gatsby relies on several technologies, including React, GraphQL, and the command-line interface (CLI). This can result in a steep learning curve, especially for developers who are new to these technologies.

  • Limited Dynamic Functionality

As a static site generator, Gatsby is designed for websites with content that doesn’t change frequently. If you have a highly dynamic site, with real-time data or user-generated content, Gatsby might not be the most suitable choice.

  • Build Time

Gatsby’s build process can be time-consuming, especially for large websites with extensive content. Generating static files for every page during the build can result in longer build times compared to server-rendered or dynamically generated websites.

  • Development Dependencies

Gatsby’s ecosystem includes various plugins, themes, and dependencies, which can occasionally lead to version conflicts or compatibility issues. Keeping up with updates and resolving conflicts can add complexity to the development process.

  • Limited Hosting Options

Gatsby generates a collection of static files that can be hosted on any web server. While this provides flexibility, it also means that you need to set up and manage your hosting environment, which might be a disadvantage if you prefer a managed hosting solution.

 

It’s worth noting that the suitability of Gatsby depends on the specific requirements of your project. Assessing these advantages and disadvantages in the context of your needs can help you determine if Gatsby is the right choice for your website or web application.

Get Started with Gatsby

To get started with Gatsby, you’ll need to have Node.js and npm (Node Package Manager) installed on your machine. Once you have those set up, follow these steps to install Gatsby and set up a new project.

 

  • Install Gatsby globally- Open your command-line interface (CLI) and run the following command
npm install -g gatsby-cli

This command installs the Gatsby CLI globally on your machine, allowing you to create and manage Gatsby projects.

  • Create a new Gatsby project- Once the Gatsby CLI is installed, navigate to the directory where you want to create your project using the CLI. For example, if you want to create a project in a folder named “my-gatsby-project,” use the following command.
gatsby new my-gatsby-project

 

This command creates a new Gatsby project in the “my-gatsby-project” folder.

  • Navigate to the project directory- After the project is created, navigate to the project directory using the CLI.
cd my-gatsby-project
  • Start the development server- To preview your Gatsby project during development, start the development server by running the following command.
gatsby develop

This command starts the Gatsby development server, which compiles your project and serves it locally at http://localhost:8000 by default.

  • Open the project in your browser: Open your web browser and visit http://localhost:8000. You should see your Gatsby site running locally.

Congratulations! You have successfully installed Gatsby and set up a new project. Now you can start building your website using Gatsby’s powerful features and ecosystem.

Companies leveraging Gatsby

 

The Gatsby framework has gained popularity among developers for building high-performance static websites and web applications. Many companies across different industries have embraced Gatsby for their web development needs. Here are a few notable companies that have leveraged the Gatsby framework.

  • Nike, a renowned global sportswear company, has used Gatsby to power its e-commerce platform. Gatsby’s speed and performance benefits make it an excellent choice for delivering a seamless shopping experience.
  • IBM, a leading technology company, has utilized Gatsby for various projects, including its developer portal and documentation sites. Gatsby’s ability to integrate with various data sources and provide fast-loading documentation has made it a suitable choice for IBM.
  • PayPal, a popular online payment platform, has employed Gatsby to build its developer documentation website. Gatsby’s capability to generate static sites with dynamic functionality is beneficial for displaying up-to-date information to developers.
  • Coursera, an online learning platform, has incorporated Gatsby for building static content pages, such as course descriptions and marketing landing pages. Gatsby’s ability to handle large amounts of content and generate fast-loading pages suits Coursera’s needs.
  • Mailchimp, an email marketing platform, has employed Gatsby to power some of its marketing pages. Gatsby’s efficient build process and content management capabilities align well with Mailchimp’s requirements.
  • Shopify, a leading e-commerce platform, has embraced Gatsby for building static content pages and marketing landing pages. Gatsby’s flexibility and performance advantages enable Shopify to deliver a great user experience.

These are just a few examples of companies leveraging the Gatsby framework. Gatsby’s scalability, speed, and ease of use have made it a popular choice for organizations looking to build performant and dynamic websites and web applications.

Use Cases Of Gatsby

 

Gatsby is a popular open-source framework for building high-performance websites and applications. It leverages modern web technologies such as React, GraphQL, and webpack to create fast, efficient, and optimized websites. Here are some common use cases for Gatsby.

  • Static websites- Gatsby is well-suited for creating static websites. It generates static HTML files for each page during the build process, resulting in fast loading times and improved SEO performance.
  • Content-rich websites- Gatsby works exceptionally well for websites that rely heavily on content, such as blogs, news sites, documentation sites, and portfolio websites. Its integration with various data sources, including CMS platforms like WordPress and headless CMSs like Contentful, allows developers to easily pull in and manage content.
  • E-commerce websites- Gatsby can be used to build e-commerce websites by integrating with e-commerce platforms like Shopify or by building custom solutions. Its static site generation capabilities combined with serverless functions enable fast and secure e-commerce experiences.
  • Progressive Web Apps (PWAs)- Gatsby can be used to create PWAs that offer app-like experiences on the web. It leverages service workers, offline support, and other progressive web technologies to enable features such as push notifications, background synchronization, and caching.
  • Landing pages and marketing sites- Gatsby’s fast performance and SEO-friendly nature make it an excellent choice for creating landing pages and marketing sites. It allows developers to build highly optimized and visually appealing pages with smooth transitions and interactive elements.
  • Personal websites and portfolios- Gatsby’s simplicity and flexibility make it a great choice for creating personal websites and portfolios. It offers numerous starter templates and themes, allowing individuals to showcase their work or personal brand with ease.

These are just a few examples of how Gatsby can be used. Its versatility and extensive plugin ecosystem make it a powerful tool for various web development projects.

Create Amazing Website UI with OnGraph

Gatsby is also well-suited for building complex websites with a lot of data, thanks to its GraphQL support. In the future, Gatsby is likely to continue to grow in popularity. As more and more developers become aware of its benefits, Gatsby is poised to become the go-to framework for building static websites.

Overall, Gatsby is a powerful and versatile framework that is well-positioned for growth in the future. If you are looking for a framework that can help you build fast, performant, and easy-to-maintain static websites, then drop us a query for the latest Gatsby development.

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Pandas AI: Shaping the Future of Data Analysis

Pandas AI

Prepare for Efficient, Automated, and Advanced Insights with Pandas-AI and witness generative AI capabilities.

Have you ever imagined that you would be able to interact with your data just like best friends? No one might have thought of it.

What if I say, you can do it now?

Well, this is what Pandas AI is for. It is an incredible Python library that empowers your data frames with the capabilities of Generative AI. the time has gone when you spent hours staring at complex rows and columns without making any meaningful progress.

So, Does it replace Panda?

Worry not, Pandas AI is not here to replace Panda, it can be considered as an extension of Panda. It comes with limitless features, imagine having a data frame that can write its own reports or one that can effortlessly analyze complex data and present you with easily understandable summaries. The possibilities are awe-inspiring!

In this concise guide, we’ll take you through a step-by-step journey of harnessing the power of this cutting-edge library, regardless of your experience level. Whether you’re an experienced data analyst or just starting out, this guide equips you with all the necessary tools to confidently dive into the world of it. 

So sit back, relax, and let’s embark on an exploration of the thrilling possibilities that it has to offer! Before we deep dive into Pandas AI, let’s brush Panda basics and key features.

What is Panda and its Key Features?

Pandas is a powerful open-source Python library that provides high-performance data manipulation and analysis tools. It introduces two fundamental data structures- DataFrame and Series, which enable efficient handling of structured data. 

Let’s explore some of the key features of pandas.

  • It provides high-performance, easy-to-use data structures like DataFrames, which are similar to tables in a relational database.
  • Panda allows you to read and write data in various formats, including CSV, Excel, SQL databases, and more.
  • It offers flexible data cleaning and preprocessing capabilities, enabling you to handle missing values, duplicate data, and other common data issues.
  • Panda provides powerful indexing and slicing functions, allowing you to extract, filter, and transform data efficiently.
  • It supports statistical operations such as grouping, aggregation, and calculation of summary statistics.
  • Panda offers a wide range of data visualization options, including line plots, scatter plots, bar charts, and histograms.
  • It integrates well with other popular Python libraries like NumPy and Matplotlib.
  • Panda is widely used in data analysis, scientific research, finance, and other fields where working with structured data is required.

It is an extension of Panda with the capabilities of generative AI, taking data analysis to another level. Now, let’s get started with it.

Pandas AI: a step ahead of data analysis game

It refers to a Python library called “Pandas AI.” It is a powerful tool that incorporates generative artificial intelligence capabilities into the popular data manipulation and analysis library called Pandas.

Introducing it, an incredible Open Source Project! It expands the power of Pandas, a Python library, by adding generative artificial intelligence features. Acting as a user-friendly interface on top of Pandas, it allows you to interact with your data effortlessly. By using smart prompts with LLMs APIs, you can transform your data into a conversational format. This means you can directly engage with your data, making data exploration more intuitive and interactive. 

The best part? With it, you don’t have to create custom in-house LLMS, saving both money and resources.

Extensive Role of Pandas AI in Data Analysis

As we have already mentioned that it is an extension of the Panda capabilities. But how? Let’s explore the role of it in improving the world of data analysis for good.

Leveraging Automation Power

It brings the power of artificial intelligence and machine learning to the existing Python Pandas library, making it a next-gen tool for simplifying data analysis. It has cut down the time analysts spent on repetitive complex tasks by automating them within minutes. Pandas enhances the productivity of analysts as they can now only focus on high-end decision-making. 

It has reduced the time and efforts of analysts in managing the below operations fall within the data analysis pipeline.

  • Data filtering
  • Data sorting
  • Data grouping
  • Data Restructuring
  • Data cleaning
  • Data integration
  • Data manipulation
  • DataFrame description
  • Data standardization
  • Time series analysis

Imagine, the implementation of AI to the above operations. Start thinking about where can you implement AI and automate your daily tasks.

Next-level Exploratory Data Analysis

When it comes to analyzing data, Exploratory Data Analysis (EDA) is a critical step. It helps analysts uncover insights, spot patterns, and catch any unusual data points. Now, imagine taking EDA to the next level with the help of Pandas AI. This incredible tool automates tasks like data profiling and visualization. It digs deep into the data, creating summary statistics and interactive visuals. This means analysts can quickly understand the nature and spread of different variables. With this automation, the data exploration process becomes faster, making it easier to discover hidden patterns and relationships efficiently.

Advanced-Data Imputation and Feature Engineering

Dealing with missing data is a frequent hurdle in data analysis, and filling in those gaps accurately can greatly affect the reliability of our findings. Here’s where Pandas AI steps in, harnessing the power of AI algorithms to cleverly impute missing values. By detecting patterns and relationships within the dataset, it fills in the gaps intelligently. 

But that’s not all! It takes a step further by automating feature engineering. It identifies and creates new variables that capture complex connections, interactions, and non-linear patterns in the data. This automated feature engineering boosts the accuracy of predictive models and saves valuable time for analysts.

Predictive Modeling and Machine Learning

Pandas AI effortlessly blends with machine learning libraries, empowering analysts to construct predictive models and unlock profound data insights. It simplifies the machine learning process by automating model selection, hyperparameter tuning, and evaluation. Analysts can now swiftly test various algorithms, assess their effectiveness, and pinpoint the best model for a specific challenge. The beauty of Pandas AI lies in its accessibility, allowing even non-coders to harness the power of machine learning for data analysis.

Accelerating Decision-making with Simulations

With Pandas AI, decision-makers gain the power to explore potential outcomes through simulations. By adjusting data and introducing different factors, this library enables users to investigate “what-if” situations and assess the effects of different strategies. By simulating real-world scenarios, Pandas AI helps make informed decisions and identify the best possible courses of action. It’s like having a crystal ball that guides you toward optimal choices.

Get Started with Pandas AI

Here’s how you can get started with Pandas, including some examples and their corresponding output.

Installation

Before you start using PandasAI, you need to install it. Open your terminal or command prompt and run the following command.

pip install pandasai

Import Pandas using OpenAI

Once you have completed the installation, you’ll need to connect to a powerful language model on the backend, the OpenAI model. To do this, you’ll need to follow these steps.

  • Visit OpenAI and sign up using your email or connect your Google Account.
  • In your Account Settings, look for “View API keys” on the left side.

 

Import Pandas using OpenAI

  • Click on “Create new Secret key”.
  • Once you have your API keys, import the required libraries into your project notebook.

These steps will allow you to obtain the necessary API key from OpenAI and set up your project notebook to connect with the OpenAI language model.

Now, you can move to import the following.

 

import pandas as pd

from pandasai import PandasAI

from pandasai.llm.openai import OpenAI

llm = OpenAI(api_token=your_API_key)

Running Model on the DataFrame with Pandas AI

Run the OpenAI model to Pandas AI, using the below command.

 

pandas_ai = PandasAI(openAImodel)

Run the model on the data frame using two parameters and ask relevant questions.

For example-

 

pandas_ai.run(df, prompt='the question you would like to ask?')

Now that we have everything in place, let’s start asking questions.

Let’s interact with DataFrames using Pandas AI

To ask questions using Pandas AI, you can use the “run” method of the PandasAI object. This method requires two inputs: the DataFrame containing your data and a natural language prompt that represents the question or commands you want to execute on your data.

To verify the accuracy of the results, we will compare the outputs from both Pandas and Pandas AI. By observing the code snippets, you can see the outcomes produced by each approach.

Querying data

You can ask PandaAI to return DataFrame rows with a column’s value greater than a specific value.

For example-

import pandas as pd

from pandasai import PandasAI

# Sample DataFrame

df = pd.DataFrame({

    "country": ["United States", "United Kingdom", "France", "Germany", "Italy", "Spain", "Canada", "Australia", "Japan", "China"],

    "gdp": [19294482071552, 2891615567872, 2411255037952, 3435817336832, 1745433788416, 1181205135360, 1607402389504, 1490967855104, 4380756541440, 14631844184064],

    "happiness_index": [6.94, 7.16, 6.66, 7.07, 6.38, 6.4, 7.23, 7.22, 5.87, 5.12]

})

# Instantiate a LLM

from pandasai.llm.openai import OpenAI

llm = OpenAI(api_token="YOUR_API_TOKEN")

pandas_ai = PandasAI(llm)

pandas_ai(df, prompt='Which are the 5 happiest countries?')
Output-

6            Canada

7         Australia

1    United Kingdom

3           Germany

0     United States

Name: country, dtype: object

Asking Complex Queries

In the above example, if you want to query to find the sum of the GDPs of the two most unhappy countries, you can run the following code.

For example-

pandas_ai(df, prompt='What is the sum of the GDPs of the 2 unhappiest countries?')
Output-

19012600725504

Data Visualization with Pandas AI

Visualizing data is essential for understanding patterns and relationships. Pandas perform data visualization tasks, such as creating plots, charts, and graphs. By visualizing data, you can gain insights and make informed decisions about AI modeling and analysis.

For example-

pandas_ai( df, "Plot the histogram of countries showing for each the gdp, using different colors for each bar", )

Data Visualization

For example-

prompt = "plot the histogram for this dataset"

response = pandas_ai.run(df, prompt=prompt)

print(f"** PANDAS AI: {response}")



Plot histogram with Pandas AI

Handling multiple DataFarmes Together Using Pandas AI

PandaAI allows you to pass multiple dataframes and ask questions based on them.

For example-

##Example of using PandasAI on multiple Pandas DataFrame

import pandas as pd

from pandasai import PandasAI

from pandasai.llm.openai import OpenAI

employees_data = {

    "EmployeeID": [1, 2, 3, 4, 5],

    "Name": ["John", "Emma", "Liam", "Olivia", "William"],

    "Department": ["HR", "Sales", "IT", "Marketing", "Finance"],

}
salaries_data = {

    "EmployeeID": [1, 2, 3, 4, 5],

    "Salary": [5000, 6000, 4500, 7000, 5500],

}

employees_df = pd.DataFrame(employees_data)

salaries_df = pd.DataFrame(salaries_data)

llm = OpenAI()

pandas_ai = PandasAI(llm, verbose=True, conversational=True)

response = pandas_ai([employees_df, salaries_df], "Who gets paid the most?")

print(response)
# Output: Olivia

Code source- GitHub

Enforcing Security

To create the Python code for execution, we first take a small portion of the dataframe, mix up the data (using random numbers for sensitive information and shuffling for non-sensitive information), and send only that portion.

If you want to protect your privacy even more, you can use PandasAI with a setting called enforce_privacy = True. This setting ensures that only the names of the columns are sent to the LLM, without sending any actual data from the data frame.

For example-

Example of using PandasAI with a Pandas DataFrame

import pandas as pd

from pandasai import PandasAI

from pandasai.llm.openai import OpenAI

from .data.sample_dataframe import dataframe

df = pd.DataFrame(dataframe)

llm = OpenAI()

pandas_ai = PandasAI(llm, verbose=True, enforce_privacy=True)

response = pandas_ai(

    df,

    "Calculate the sum of the gdp of north american countries",

)

print(response)
# Output: 20901884461056

Code source- GitHub

Pandas AI with other LLMs

GooglePalm

PaLM 2 is a new and improved language model made by Google. It’s really good at doing advanced thinking tasks like understanding code and math, answering questions, translating languages, and creating natural-sounding sentences. It’s even better at these things than our previous language models. We made it this way by using better technology and improving how it learns from data.

To use this model, you can get the Google Cloud API Key. After getting the key. Create an instance for the Google PaLM object.

Use the below example to call GooglePalm Model

from pandasai import PandasAI

from pandasai.llm.google_palm import GooglePalm

llm = GooglePalm(google_cloud_api_key="my-google-cloud-api-key")

pandas_ai = PandasAI(llm=llm)

Google VertexAI

If you want to use the Google PaLM models through Vertexai api, then you must have the following.

  • Google Cloud Project
  • Region of Project Set up
  • Install optional dependency google-cloud-aiplatform
  • Authentication of gcloud

After setting everything, then you can create the instance for Google PaLM using VertexAI. Use the below example to call Google VertexAI.

from pandasai import PandasAI

from pandasai.llm.google_palm import GoogleVertexai

llm = GoogleVertexai(project_id="generative-ai-training",

                     location="us-central1",

                     model="text-bison@001")

pandas_ai = PandasAI(llm=llm)

HuggingFace models

Same as OpenAI, you also need a HuggingFace models

 To use this model. You can get the key. 

Use the key for instantiating the HuggingFace models. PandasAI supports the following HuggingFace models-

  • Starcoder: bigcode/starcoder
  • OpenAssistant: OpenAssistant/oasst-sft-1-pythia-12b
  • Falcon: tiiuae/falcon-7b-instruct

 

For example-

 

from pandasai import PandasAI

from pandasai.llm.starcoder import Starcoder

from pandasai.llm.open_assistant import OpenAssistant

from pandasai.llm.falcon import Falcon

llm = Starcoder(huggingface_api_key="my-huggingface-api-key")

# or

llm = OpenAssistant(huggingface_api_key="my-huggingface-api-key")

# or

llm = Falcon(huggingface_api_key="my-huggingface-api-key")

pandas_ai = PandasAI(llm=llm)
  • If you want to continue without the key, then you can use the following method by setting the HUGGINGFACE_API_KEY environment variable.
from pandasai import PandasAI

from pandasai.llm.starcoder import Starcoder

from pandasai.llm.open_assistant import OpenAssistant

from pandasai.llm.falcon import Falcon

llm = Starcoder() # no need to pass the API key, it will be read from the environment variable

# or

llm = OpenAssistant() # no need to pass the API key, it will be read from the environment variable

# or

llm = Falcon() # no need to pass the API key, it will be read from the environment variable

pandas_ai = PandasAI(llm=llm)

Challenges Ahead of Pandas AI

As we delve into Pandas AI and its potential to transform data analysis, it’s crucial to address certain challenges and ethical considerations. Automating data analysis highlights important concerns regarding transparency, accountability, and bias. Analysts need to be cautious when interpreting and validating the results produced by Pandas AI, as they retain the responsibility for critical decision-making based on the insights derived. 

Let’s remember that while Pandas AI offers incredible possibilities, human judgment, and careful assessment remain indispensable for making informed choices.

Below are some other challenges that you must consider for better data analysis.

  • Interpretation of Prompts- The results generated by Pandas AI heavily rely on how the AI interprets the prompts given by users. In some cases, it may not provide the expected answers, leading to potential discrepancies or confusion.
  • Contextual Understanding- Pandas AI may struggle with understanding the contextual nuances of specific datasets or domain-specific terminology. This can sometimes result in inaccurate or incomplete insights.
  • Limited Coverage- Pandas AI’s effectiveness is influenced by the breadth and depth of its training data. If the library hasn’t been extensively trained on certain types of datasets or domains, its performance in those areas may be limited.
  • Handling Ambiguity- Ambiguous or poorly defined prompts can pose challenges for Pandas AI, potentially leading to inconsistent or unreliable outcomes. Clear and precise instructions are crucial to ensure accurate results.
  • Dependency on Training Data- The quality and diversity of the training data used to develop Pandas AI can impact its performance. Biases or limitations in the training data may influence the library’s ability to handle certain scenarios or produce unbiased insights.

Consider potential challenges and exercise caution when relying on Pandas AI for critical decision-making or sensitive data analysis. Consistent evaluation and validation of the generated results help mitigate these challenges and ensure the reliability of the analysis.

Pandas AI with Solid Future Prospects

PandasAI holds the potential to revolutionize the ever-changing world of data analysis. If you’re a data analyst focused on extracting insights and creating plots based on user needs, this library can automate the process efficiently. However, there are a few challenges to be aware of while using PandasAI.

The results obtained heavily rely on how the AI interprets your instructions, and sometimes it may not give the expected answers. For example, in the Olympics dataset, the AI occasionally got confused between “Olympic games” and “Olympic events,” leading to potentially different responses. 

Nevertheless, its advantages in simplifying and streamlining data analysis make it a valuable tool. It’s advanced functionalities and efficient capabilities are indispensable assets in a data scientist’s toolkit.

FAQs

Q1: What is Pandas AI and how can it help me with my data analysis?

Pandas AI is an enhanced representation of Pandas library, which applies artificial intelligence (AI) to make data analysis easier and quicker. It performs tasks such as data cleaning automatically and offers smarter insights with better visualizations.

Q2: How do the traditional Pandas differ from the AI version?

This software goes beyond the usual pandas by incorporating artificial intelligence into its features. For instance, it automates data cleaning, has advanced visualizations, offers predictive analytics, and allows for querying of data in natural language.

Q3: Can I use this tool alongside other tools in my existing workflow?

Yes, you can use this along with other tools like the Traditional Pandas library itself, NumPy, Matplotlib, or Seaborn without any issues.

Q4: Who can benefit from using Pandas AI? 

Pandas AI is beneficial for:

  • Data Scientists and Analysts
  • Business Analysts
  • Researchers
  • Developers

Q5: What are the advantages of using Pandas AI over traditional Pandas? 

Advantages of Pandas AI over traditional Pandas include:

  • Increased Automation: Reduces the need for manual data preparation.
  • Enhanced Insights: Provides deeper and more accurate analysis using AI.
  • Time Savings: Speeds up the data analysis process significantly.
  • User-Friendly: Easier to use for both novice and experienced users with its NLP features.
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Python 3.12: Faster, Leaner, and More Powerful

Python

Python, the ever-evolving and versatile programming language, continues to deliver cleaner and more powerful versions with each release. The latest installment, Python 3.12, promises groundbreaking improvements that are set to revolutionize the programming landscape. Let’s delve into the exciting advancements and features that await developers in Python 3.12.

PyCon 2023 Showcases Python’s Promising Future

The recent PyCon 2023 event shed light on the promising future of Python, captivating developers with its potential to facilitate faster and more efficient software development. Python 3.12 is anticipated to bring forth a series of advancements that will pave the way for innovation and optimization.

Memory Usage Optimization

Python 3.12 introduces impressive optimizations, with influential figures like Mark Shannon and other notable speakers addressing various challenges faced by Python. One of the key achievements is a significant reduction in Python’s memory usage. 

The object header, which previously occupied 208 bytes, has now been minimized to a mere 96 bytes. This improvement provides ample space for storing objects in memory, leading to enhanced performance.

Support for subinterpreters

Subinterpreters are a new feature in Python 3.12 that allows developers to run multiple independent Python interpreters within a single process. This can be useful for tasks such as testing and debugging. For example, a developer could use subinterpreters to run a test suite in a separate interpreter or to debug a program in a separate interpreter without affecting the main interpreter.

Adaptive specialization

Adaptive specialization is a new feature in Python 3.12 that allows the Python interpreter to generate more efficient code for specific types of data. This can improve performance for certain types of applications. For example, if a program frequently performs operations on a large array of numbers, the Python interpreter can specialize the code for those operations to make them faster.

Improved error messages

The error messages in Python 3.12 have been improved, making it easier for developers to debug their code. For example, error messages now include more information about the source of the error, which can help developers to identify and fix the problem.

Enhancing CPython’s Stability and Compatibility

Python 3.12 prioritizes stability and compatibility by refining CPython’s numerous C APIs. Core Python developer Victor Stinner emphasizes keeping public APIs private to minimize dependencies on potential version changes. Additionally, the third-party project HPy offers a more stable C API for Python, benefiting influential projects like NumPy and ultrajson.

Some of the highlights of Python 3.12

Python 3.12 introduces several enhancements that make programming with the language easier and more efficient. These improvements include.

  • The simplified syntax for generic classes- Writing generic classes, which enable code reuse and efficiency, is now more straightforward with the new type annotation syntax.
  • Increased flexibility in f-string parsing- F-strings, a way to format strings, now offer greater versatility, allowing for more powerful and expressive usage.
  • Enhanced error messages- Python 3.12 features even more improved error messages, making it simpler to understand and fix issues in your code.
  • Performance enhancements- Many significant and minor optimizations have been made in Python 3.12, resulting in faster and more efficient execution compared to previous versions.
  • Support for Linux perf profiler- With the inclusion of the Linux perf profiler, it is now easier to profile Python code and obtain function names in traces.

Partner with OnGraph for Cutting-Edge Python Development

To stay ahead of the competition and leverage the capabilities of Python 3.12, consider partnering with OnGraph, a leading provider of next-generation Python development services. With their expertise and in-depth knowledge of the latest Python version, OnGraph ensures that your projects are at the forefront of innovation.

 

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Remix: The Next-Gen React Framework For Faster Websites

Remix feature

Remix with its strict focus on web standards allows it to meet the needs of modern-age web app user experience. So, get ready to build faster and better websites with old-school techniques.

With its formal release in October 2021, Remix is at the top of the list of every UX designer who wants to develop out-of-the-box designs. 

Remix stats

 Image Credits: betterprogramming.pub

Staying ahead of the competition and delivering outstanding user experience is becoming one of the topmost priorities of businesses to scale. If you are still unaware of what Remix is and how it can help your websites run faster, then you are on the right track. 

So, let’s get you started with this detailed guide to Remix. 

What is Remix?

Remix is a cutting-edge JavaScript framework that redefines the way developers build web applications. Developed with a focus on performance, flexibility, and developer productivity, Remix offers a comprehensive solution for building modern, scalable, and maintainable web projects. 

Powered by React, Remix leverages the best practices of server-side rendering and client-side rendering, providing a seamless experience for users and search engines alike. With Remix, you can easily create dynamic and interactive web experiences while ensuring optimal performance and search engine optimization. 

Its intuitive and component-based architecture, combined with powerful routing capabilities, enables you to build robust and feature-rich applications with ease. Whether you’re starting a new project or migrating an existing one, Remix empowers you to deliver exceptional web experiences that delight your users.

What to Expect from Remix?

  • It can be compiled using esbuild, a speedy tool for bundling and minimizing JavaScript/CSS.
  • The server side of the application follows progressive enhancement, meaning it only sends essential JavaScript, JSON, and CSS to the browser.
  • It can dynamically render content on the server side.
  • It has the ability to recognize when to update data that has been changed, thanks to Remix overseeing the entire process.
  • It provides a comprehensive solution that includes React Router, server-side rendering, a production server, and optimization for the backend.

As businesses and developers/designers are pushing the boundaries of the web and its applications, existing tools seem to have some restrictions. With Remix, all your fancy UX ideas will come true.

Why did Remix come into the picture?

For that, we are highlighting how the website was created earlier to understand the exact need to use Remix. In the early days, web pages were primarily made up of plain HTML. If developers needed to update data, they would add a form to send the data to the server.

Over time, frameworks were created to allow developers to incorporate dynamic data into static templates, ensuring users always had up-to-date information. PHP was commonly used for this purpose, with PHP tags inserted into HTML files to insert dynamic content from external sources.

However, as developers embraced the concept of “separation of concerns,” mixing PHP, HTML, JavaScript, and CSS in the same file became burdensome. PHP templating lost popularity as JavaScript frameworks like Node and React gained traction, and specialized roles like front-end and back-end developers emerged.

But as web development progressed, the idea of splitting a single page into multiple files became cumbersome. Developers began to explore the use of CSS-in-JS, loaders for dynamic information, and actions for data manipulation. This led to the emergence of React Remix.

React Remix, built on top of React, doesn’t disrupt current patterns but introduces paradigm shifts. Unlike React, which is a frontend library, React Remix, along with competitors like Next.js and Gatsby, aims to enable server-side rendering (SSR). It benefits developers seeking SSR advantages and can be seen as the evolution of old ASP.net and PHP frameworks.

How is Remix different from other Frameworks?

Let us help you understand how Remix can improve the user experience of your web apps like no other framework can.

Nested Routes 

Every website has multiple levels of navigation that control the child’s views. You can see that these components are mostly coupled to the URL segments. On top of it, these components define the semantic boundary of the data loading and the code splitting. 

In the below example, you can see the flow of the URL- example.com/sales/invoices/102000.

Where-

  • example.com defines the root.
  • Sales define an internal component of the root
  • Invoices are the internal component of sales.
  • And the last is the invoice_id which is the child component of invoices. 

 

nested Routes in Remix

Image Credits: Remix.run

In general, most web apps fetch internal components, leading to a waterfall request model where one component will load after the previous one is done loading. It results in slower web apps and long loading times.

Using nested routes, Remix successfully degrades the loading state of each component. It loads the data in parallel on the server and sends the completely formatted and loaded HTML document at once, leading to faster loading.

 

before and after Remix

Image Credits: Remix.run

Without Remix, loading will waterfall requests, while with Remix, the complete document will load along with its components in parallel. Remix prefetches the entire data (Public Data. User Data. Modules. heck, even CSS.) in parallel even before the user clicks the URL, leading to zero loading states. 

Data Loading in Remix

In general what your code does? It changes data, right? What if you only have props but there is no way that you can set the state? If your framework does not let you update the data that you have loaded from different sources, then what’s the purpose? Well, Remix does not do that. It allows you to update data with its built-in data updates.

Let us explain to you with a simple example.

 

export default function NewInvoice() {
  return (
    <Form method="post">
      <input type="text" name="company" />
      <input type="text" name="amount" />
      <button type="submit">Create</button>
    </Form>
  );
}

Now, we will add an action to this route module. At first glance, it will look like an HTML form but you will get a next-level fully dynamic user experience that you have exactly in mind.

 

export default function NewInvoice() {
  return (
    <Form method="post">
      <input type="text" name="company" />
      <input type="text" name="amount" />
      <button type="submit">Create</button>
    </Form>
  );
}

export async function action({ request }) {
  const body = await request.formData();
  const invoice = await createInvoice(body);

Remix successfully runs the required action on the server side, then revalidates the data with the client side. Not only this, the Remix will handle the race conditions from getting re-submitted. 

 

Rexim running requests

Image Credits: Remix.run

Remix uses transition hooks to make the pending UI. it can handle all the states simultaneously.

export default function NewInvoice() {
  const navigation = useNavigation();
  return (
    <Form method="post">
      <input type="text" name="company" />
      <input type="text" name="amount" />
      <button type="submit">
        {navigation.state === "submitting"
          ? "Creating invoice..."
          : "Create invoice"}
      </button>
    </Form>
  );

Apart from this, Remix allows the data to be transferred to the server for skipping the busy spinners for mutations. 

 

export default function NewInvoice() {
  const { formData } = useNavigation();
  return submission ? (
    <Invoice
      invoice={Object.fromEntries(formData)}
    />
  ) : (
    <Form method="post">
      <input type="text" name="company" />
      <input type="text" name="amount" />
      <button type="submit">
        Create invoice
      </button>

Handling Errors 

It is obvious that the websites run into errors. But with Remix, the good thing is that you do not have to refresh the website. Keeping the complexity of handling errors in mind, Remix comes with built-in error-handling features. 

Remix is capable of handling errors during server rendering, client rendering, and even server-side data handling. In most frameworks, if there’s an error in a part of a webpage or if a specific section fails to load, the entire page breaks, and an error message is displayed.

Error handling without Remix

Image Credits: Remix.run

 

However, in Remix, if we make a component or a route, we can set up a special error template that handles any errors that occur in that specific component or route. When an error happens, instead of seeing the actual component or route, we’ll see this customized error template. And the error will only affect that specific component or route, without breaking the whole page.

 

Remix error handling

Image Credits: Remix.run

 

SEO with Meta Tags

In simple terms, Remix allows us to customize the information that appears in search results and social media previews for each section of our website. We can do this by using a special component called Meta, which we place in the header of our web page.

The Meta component adds the specific information we want to show, such as the page title, description, and social media links. To make it work, we need to create a function called export meta that returns an object with the desired information.

When we visit a page on our website, the Meta component checks if there’s a meta function defined for that page. If there is, it uses that function to add the custom data to the header of the HTML document. And when we leave that page, it automatically removes the added information.

 

import { Meta } from 'remix'
export const meta = () => {
return {
title: 'A title for this route',
description: 'A description for the route',
keywords: 'remix, javascript, react'
}
}

export default function App() {
return (
<html lang="en">
<head>
<Meta />
</head>
<body>
<h1>Testing SEO Tags</h1>
</body>
</html>
)
}

In the above example, the head is empty, with the meta component. This meta function will look for an exported meta function and fills the data into the head. 

On running the above code, the source code will look like this.

Remix- SEO with Meta Tags

Image Credits- bejamas.io

 

Styling in Remix

Remix uses a traditional method of linking to a stylesheet for styling a particular page. Similar to setting SEO meta tags, we can assign a stylesheet dynamically to each page using a special component called <Links/>.

With the help of the <Links/> component, we can load a specific stylesheet for a particular page. We need to define a function called “links” that exports an array that stores information about each stylesheet we want to use on the page. These stylesheets will be removed automatically when we leave that page.

For creating a stylesheet, create a directory called “styles” in our app. Inside this directory, we can create a file called “global.css” for styles that apply to the entire app, or we can manually create separate stylesheets for each page.

Remix Styling

Image Credits- bejamas.io

 

For using this stylesheet, you can use the below code.

 

import { Links } from 'remix'
import globalStyleURL from '~/styles/global.css'
export const links = () => {
return [{ rel: 'stylesheet', href: globalStyleURL }]
}

export default function App() {
return (
<html lang="en">
<head>
<title>Just a title</title>
<Link />
</head>
<body>
<h1>Testing Styling</h1>
</body>
</html>
)
}

On checking the source code, you will find that the stylesheet is available in your app as a link tag.

Forms in Remix

Remix connects forms to the application’s state and handles form submissions in React. Instead of manually linking forms to the state and handling submissions with event listeners. An action function automatically gets the form data after submission. It utilizes standard “post” and “get” request methods to send and change the form data just like PHP.

When you submit a form, it triggers the action function that handles the submission. By default, the form data will be sent to the action handler function via the request object. The action function executes on the server, enabling easy communication with a database using the form details. This eliminates the need for client-side mutations.

You can create a form in Remix using either the HTML form element (“<form>”) or import a Remix’s Form component. Unlike traditional form elements, this Form component uses the fetch API for sending the form data, which is faster. The entered data will be sent to the action function that you can access within the action function via input field names.

Let’s create a basic form by utilizing the new.jsx route component in the “posts” directory.

 

import { Form, redirect } from 'remix'
export const action = async ({ request }) => {
const form = await request.formData()
const title = form.get('title')
const content = form.get('content')
console.log({ title, content })
return redirect('/')

}

export default function NewPost() {
return (
<div>
<h1>Add a new post</h1>
<Form method="POST">
<label htmlFor="title">
 Title: <input type="text" name="title" />
</label>
<label htmlFor="content">
 Content: <textarea name="content" />
</label>
<input type="submit" value="Add New" />
</Form>
</div>
)
}

Did you notice that we brought in a function from Remix called “redirect”? This function works similarly to the redirect function in react-router.

This function tells Remix that after the form is submitted, it should send the user to the index route, which is the homepage. Normally, we would use this to update a database with the form data, but for the sake of simplicity, we will just log to the server’s console. Keep in mind that this action function only runs on the server. So let’s go ahead and do that.

 

Remix Forms

Image Credits- bejamas.io

Output-

 

Forms output

Image Credits- bejamas.io

 

It’s important to understand that when you submit a form using the “post” method, it is automatically handled by the action function given in the component. However, if you choose to submit the form using the “get” method, Remix (a tool or framework) requires you to define a loader function to handle the form data on the server.

Are there any limitations limited to Remix?

The Remix framework, like any other tool or framework, has certain limitations. Here are some of the limitations of the Remix framework.

 

  • Learning curve- Remix is a relatively new framework, and as such, there may be a learning curve involved in understanding its concepts and best practices. Developers who are already familiar with other frameworks may need some time to adapt to Remix’s specific way of doing things.
  • Limited community support- Compared to more established frameworks like React or Angular, the Remix community might be smaller, which means there may be fewer resources, tutorials, and community support available. This could make troubleshooting and finding solutions to specific issues more challenging.
  • Restricted ecosystem- The Remix framework has a specific ecosystem of plugins, libraries, and tools. While it offers a robust set of features, the range of available integrations and extensions might be more limited compared to more mature frameworks with larger ecosystems.
  • Compatibility with existing codebases– If you already have an existing codebase built on a different framework, migrating it to Remix might require significant effort and refactoring. Remix follows its own conventions and patterns, so adapting an existing codebase might not be a straightforward process.
  • Limited adoption– As of now, Remix may not have gained widespread adoption in the developer community. This means that finding developers experienced in Remix might be more difficult, and collaborating on projects using Remix could be challenging if team members are unfamiliar with the framework.

Build next-gen Remix apps with OnGraph

The Remix framework exhibits immense potential for shaping the future of web development. With its innovative approach to building modern applications, Remix enables developers to create robust, scalable, and performant experiences for users. 

As the demand for fast, interactive, and accessible web applications continues to grow, Remix stands poised to play a significant role in driving this evolution. With its focus on developer productivity, code maintainability, and seamless integration with existing technologies, Remix paves the way for a future where building cutting-edge web applications becomes more efficient, enjoyable, and impactful than ever before. Looking for a next-gen, fast, and smooth Remix application? Let’s connect for a call today with one of our solution architects and build the next app with us.

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Exploring the Future of Artificial Intelligence: Insights, Innovations, Impacts, and Challenges

AI

Have you ever imagined that machines could also think and act like humans? No, right! Well, now everything is possible with artificial intelligence. It has gained immense attention from across the globe, and companies are willing to adopt it to transform digitally and smartly. You can consider it a wind that swept the whole market with its limitless features and efficiency to eliminate manual jobs. The Artificial Intelligence market is growing like anything and is capturing a considerable market sector, including different industrial sectors. So, will it cut down the job opportunities? It can be true or not. It depends on what we are expecting it to do. 

According to Forbes, businesses leveraging AI and related technologies like machine learning and deep learning tend to unlock new business opportunities and make huge profits than competitors.

Over the years, AI has evolved gracefully and helped businesses work efficiently. This article will focus on what AI is, how it evolved, its challenges, and its promising future. 

Artificial Intelligence business based on insights

What is AI (Artificial Intelligence)?

Artificial intelligence significantly deals with the simulation of intelligent behavior in computers. In simple words, artificial intelligence is when machines start acting intelligently, taking considerable decisions like humans, and making focused decisions. 

Today, we hear terms like machine learning, deep learning, and AI. all are interconnected and embrace each other for improved productivity.

AI (Artificial Intelligence)

We all are eager to know what started this beautiful and promising technology helping the human race. But from where did the AI’s journey start? So, let’s dig into the past.

When did Artificial Intelligence start to rise? 

The roots of Artificial Intelligence (AI) can be traced back to ancient times when individuals began to contemplate the idea of creating intelligent machines. However, the modern field of AI, as we know it today, was formulated in the mid-20th century.

  • The first half of the 20th century saw the emergence of the concept of AI, starting with the humanoid robot in the movie Metropolis. In 1950, prominent scientists and mathematicians began to delve into AI, including Alan Turing, who explored the mathematical possibility of creating intelligent machines. He posited that since humans use information to make decisions and solve problems, why couldn’t machines do the same thing? This idea was further expounded in his paper, “Computing Machinery and Intelligence,” which discussed the building and testing of intelligent machines.

 

  • Unfortunately, Turing’s work was limited by the technology of the time, as computers could not store commands and were costly, hindering further research. Five years later, Allen Newell, Cliff Shaw, and Herbert Simon initiated the proof of concept with the “Logic Theories” program, which mimicked human problem-solving skills and was funded by the RAND Corporation. This first AI program was presented at the Dartmouth Summer Research Project on Artificial Intelligence in 1956.

 

  • From 1957 to 1974, AI continued to advance as the challenges that had hindered Turing’s work became solvable. Computers became more affordable and were able to store information. Additionally, machine learning algorithms improved, allowing researchers to determine which algorithms were best suited for different scenarios. Early demonstrations such as the “General Problem Solver” by Newell and Simon and Joseph Weizenbaum’s “ELIZA” showed promising problem-solving and language interpretation results, resulting in increased AI research funding.

With the common challenge of computational power to do anything substantial: computers simply couldn’t store enough information or process it fast enough. 

  • The 1980s saw a resurgence of interest in AI with the expansion of algorithmic tools and increased funding. John Hopfield and David Rumelhart introduced the concept of “deep learning,” allowing computers to learn based on prior experience, while Edward Feigenbaum created expert systems that replicated human decision-making.

 

  • The Japanese government heavily invested in AI through their Fifth Generation Computer Project (FGCP) from 1982 to 1990, spending 400 million dollars on improving computer processing, logic programming, and AI.

 

  • In the 1990s and 2000s, many significant milestones in AI were reached. In 1997, IBM’s Deep Blue defeated reigning world chess champion, Gary Kasparov, marking a significant step towards artificial decision-making programs. That same year, Dragon Systems developed speech recognition software for Windows, further advancing the field of spoken language interpretation. 

The fact holding us back has not been a problem anymore. Moore’s law estimating that the memory and speed of computers double every year has been solved this year. 

AI is a revolution that is now a top demand in the market. AI is not a single step; many things have happened and been introduced in the past that make AI stronger with time. So, what are those revolutions? Let’s check.

Artificial Intelligence Revolution

The AI revolution refers to the rapidly evolving field of Artificial Intelligence (AI) and its growing impact on society. The AI revolution is characterized by a rapid increase in the development and deployment of AI technologies, leading to numerous benefits and challenges.

Artificial Intelligence Revolution

Some of the critical aspects of the AI revolution include the following.

  • Advancements in AI technologies: The development of AI technologies has continued to advance rapidly in recent years, with breakthroughs in deep learning, computer vision, and natural language processing.
  • Increased Automation: AI technologies are being used to automate routine and repetitive tasks, freeing human workers for more strategic tasks and increasing efficiency in various industries.
  • Improved Decision-Making: AI systems are used to analyze large amounts of data, enabling more accurate and efficient decision-making in various industries, such as finance, healthcare, and retail.
  • Increased Personalization: AI technologies provide personalized experiences, such as personalized recommendations and customized advertisements.
  • Ethical and Legal Concerns: As AI technologies continue to advance and impact society, ethical and legal concerns have become increasingly important, such as issues related to data privacy, bias, and accountability.

Overall, the AI revolution is transforming numerous industries and has the potential to bring about significant benefits and challenges in the coming years. 

Here are some of the key developments in AI from recent years up to 2023:

  • Deep Learning Advancements: Deep learning, a subfield of machine learning, has made breakthroughs in recent years, with deep neural networks achieving state-of-the-art results in tasks such as computer vision, natural language processing, and speech recognition.
  • Natural Language Processing: it enables machines to understand and generate human-like language with increasing accuracy. Today, companies are integrating NLP technologies into virtual assistants, chatbots, and customer service systems.
  • Computer Vision: Computer vision technologies have made significant progress, allowing machines to recognize and understand visual information in images and videos with increasing accuracy, leading to the development of self-driving cars, facial recognition systems, object recognition systems, etc.
  • Robotic Process Automation: Robotic process automation (RPA) has become increasingly popular in recent years, allowing organizations to automate routine and repetitive tasks, freeing up human workers for more strategic tasks.
  • Generative Adversarial Networks (GANs): GANs have become an essential area of research in recent years, allowing machines to generate new data, such as images, videos, and music, based on a set of training data.
  • Explainable AI (XAI): With the increasing deployment of AI systems in critical applications, the need for explainable AI has become increasingly important. XAI aims to make AI systems more transparent and interpretable, allowing decision-makers to understand how AI systems make decisions.

Today, most people estimate and fear that AI will take their jobs and that machines will replace human beings in the coming time. Looking at the scenarios, most jobs are at risk as automation reduces human work. Being based on data and accessing data from different sources, how safe is AI? What are the risks, security, and trust associated with AI?

Let’s see.

Artificial Intelligence — Trust, Risk & Security (AI TRISM)

We trust artificial intelligence for personal and business functions, but how far can we trust it? With significant business and healthcare decisions on the line, is it wise to trust a computer? Despite concerns, inaccuracies, design flaws, and security, many companies still need help fully trusting AI. With significant business and healthcare decisions on the line, is it wise to trust a computer? 

Companies must adopt a tool portfolio approach to address these concerns, as most AI platforms do not provide all the necessary features.

Gartner® has introduced the concept of AI Trust, Risk, and Security Management (AI TRiSM) to address these issues. Companies can implement AI TRiSM by utilizing cross-disciplinary practices and methodologies to evaluate and secure AI models. Here is a framework for managing trust, risk, and security in AI models.

Artificial Intelligence TRISM

Implementing AI Trust, Risk, and Security Management (AI TRiSM) requires a comprehensive approach to ensuring a balance between managing risks and promoting trust in the technology. This approach can be applied to various AI models, including open-source models like ChatGPT and proprietary enterprise models. However, there may be differences in the application of AI TRiSM for open-source models, such as protecting the confidential training data used to update the model for specific enterprise needs.

The key components of AI TRiSM include a range of methods and tools that can be tailored to specific AI models. To effectively implement AI TRiSM, it is essential to have core capabilities that address the management of trust, risk, and security in AI technology.

Artificial Intelligence TRISM Market

  • Explainability: The AI TRiSM strategy must include information explaining the AI technology’s purpose. We must describe the objectives, advantages, disadvantages, expected behaviour, and potential biases to help in clarifying how a specific AI model will ensure accuracy, accountability, fairness, stability, and transparency in decision-making.
  • Model Operations (ModelOps): The ModelOps component of the AI TRiSM strategy covers the governance and lifecycle management of all AI models, including analytical and machine learning models.
  • Data Anomaly Detection: The objective of Data Anomaly Detection in AI TRiSM is to detect any changes or deviations in the critical features of data, which could result in errors, bias, or attacks in the AI process. This ensures that data issues and anomalies are detected and addressed before decisions are made based on the information provided by the AI model.
  • Adversarial Attack Resistance in AI TRiSM is designed to protect machine learning algorithms from being altered by adversarial attacks that could harm organizations. This is achieved by making the models resistant to adversarial inputs throughout their entire lifecycle, from development, and testing, to implementation. For example, a technique for attack resistance may be implemented to enable the model to withstand a certain noise level, as it could potentially be adversarial input.
  • Data Protection: The protection of the large amounts of data required by AI technology is critical during implementation. As part of AI TRiSM, data protection is critical in regulated industries, such as healthcare and finance. Organizations must comply with regulations like HIPAA in the US and GDPR or face non-compliance consequences. Additionally, regulators currently focus on AI-specific regulations, particularly regarding protecting privacy.

Achieving AI TRISM can be complicated. Here is the roadmap that any business can consider for the AI market.

Artificial Intelligence TRISM Market future direction

Undoubtedly, AI has a bright future and a growing market. 

The promising future of Artificial Intelligence in 2023 and Beyond

There is increasing hype about AI and its implementation. Thus continuous advancements and development can be seen in the field of AI.

The future of AI in 2023 and beyond is poised to bring about significant advancements and transformations in various industries and aspects of daily life. Some key trends and predictions for the future of AI include the following.

  • AI for Business: AI is expected to play an increasingly important role in businesses, with the adoption of AI technologies for tasks such as automation, process optimization, and decision-making.
  • Advancements in Natural Language Processing (NLP): NLP is set to become even more advanced, enabling AI systems to understand and interpret human language more accurately and efficiently.
  • Integration with IoT: AI with the Internet of Things (IoT) is expected to lead to the creation of smart homes, factories, and cities, where devices and systems can work together to create a seamless and efficient experience.
  • Growth of AI in Healthcare: AI is expected to revolutionize the healthcare industry using AI technologies for drug discovery, diagnosis, and patient monitoring.
  • Ethics and Responsibility: As AI becomes more prevalent, there will be a growing focus on AI’s ethical and responsible use, including the need for transparency and accountability in AI decision-making.

Challenges Ahead of Artificial Intelligence

Today, humans are driving AI and making innovations, but what if the table turns and humans become the puppet of machines?

Sounds horrendous, right? Well, if technology keeps on advancing like this, then there is no time left for people to become highly reliant on machines. But what made us think like that?

High-profile names of the market, Elon Musk, and Steve Wozniak suggested that companies and labs must give a pause of six months to train AI systems that are stronger than GPT-4. These two have circulated an open letter stating how AI can impact the human race and create a human-competitive era, which could change the whole truth of existence. 

Also, in the recent news, the CEO of OpenAI, Sam Altman brings up the crucial point for the US government to regulate Artificial Intelligence. He also mentioned forming an agency that takes care of licenses for all AI-based companies to ensure accuracy. As per him, the technology is good but if it goes wrong it can do worse. 

So, it is better to play safe with AI and not take unnecessary advantage of such technologies that can impact the human world.

Wrapping up

Overall, the future of AI is promising and holds the potential to bring about positive changes in many areas of society. However, it is essential to ensure that AI is developed and used responsibly, with considerations for ethical and social implications.

AI innovations continue to deliver significant benefits to businesses, and adoption rates will accelerate in the coming years. But, make sure that you implement AI to a certain limit to which businesses can handle the automation and still be in charge of major changes.

If you want to develop a next-gen AI app or solution, you can connect with us. Drop us a query today.

Also, stay tuned to our website for more interesting news and the latest trends around AI.

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Vite- The Next-gen Blazing-fast Front-end development

Vite.js

Vite, a rapid tool for project scaffolding and bundling – gains popularity with its speedy code compilation and instant module replacement. Discover Vite’s ultimate features while building your first App in this article.

With the availability of several tools and in the digital transformation era, every process has evolved. Then why not improve? We used to create projects with manual efforts of creating folders and transferring files using FTP.

Now developers have access to amazing tools and technologies to improve their development experiences, such as Babel and Webpack. But, to get along with changing business demands, we have to explore more new tools to deliver the best.

This brings our notice to a blazing-fast front-end development environment introduced in 2020. Since then it has gained much popularity and become the fastest tool for a seamless web development experience. Its features-rich CLI has made it easier for scaffolding projects.

But among the different options available in the market, why must developers choose Vite? What makes it more powerful and fast?

As we embark on this journey, we’ll delve into the realm of Vite and discover why it deserves our undivided attention. Let’s ensure we stay in sync with the ever-changing times and embrace this exciting new tool. So, without further ado, let’s dive into the world of Vite!

What is Vite?

Did you know that the word “Vite” comes from the French language and it means “quickly” or “fast”? Pronounced as “Vit,” this word perfectly captures the essence of what Evan You had in mind while working on Vue3, the popular JavaScript framework.

Vite is a revolutionary development environment for Vue.js, created by none other than Evan You himself. Its purpose is to make web development as fast as possible, pushing the boundaries of what we thought was achievable. Although still in the experimental phase, the community is actively working towards refining Vite, making it suitable for production environments.

What makes Vite so special is that it eliminates the need for a traditional bundler. Instead, it serves your code through native ES Module imports during development. This means that you can effortlessly work on Vue.js single file components without the hassle of bundling them together. However, Vite cleverly utilizes Rollup for efficient bundling when it comes to a production build.

With Vite, the possibilities for web development become endless. It empowers developers to focus on what truly matters—creating amazing experiences—without being hindered by time-consuming bundling processes. Evan You’s innovative creation is reshaping the way we approach Vue.js development, and the web development community is eagerly embracing this groundbreaking tool.

But do you wonder how it does it?

How did Vite come into the picture?

I guess without highlighting what crucial web development problems result in the development of this amazing front-end tool. Right! So what was the actual problem that developers have been facing before Vite?

The Problem

Before developers could leverage the ES modules within the browsers, there were no specific methods to author the JavaScript in a modular way. This is how the term “bundling” was introduced which uses the relevant tools to crawl, process, and connect different source modules within a single file to be run in the browser. 

If you are getting me, then you must know about the tools, such as webpack, Rollup, and Parcel. These tools are popularly known for their performance to improve the efficiency of developers in developing the front end. 

It is obvious that developers try to create more complex applications or apps get complicated with time, resulting in increased usage of JavaScript. If any JavaScript app scales, it will contain more modules that create performance nuances for JavaScript-based tools. 

It results in a long wait time to sign up into the related dev server, Hot Module Replacement, and to create/edit files in the browser. This slow speed was impacting the developer’s productivity. 

This is why developers need fast and extremely reliable front-end tooling.

Vite successfully addresses all these speed-related issues. All thanks to the availability of native ES modules in the browser, and the rise of JavaScript tools written in compile-to-native languages.

How does Vite work to deal with Slow Server Start?

Whenever you try to cold-start your dev server, your entire app will be crawled and built by a bundler-based build setup before serving it to the browser.

Vite is capable of improving and fastening the dev server start time. But how?

It divides the app modules into two categories- dependencies and source code.

  • Most JavaScript dependencies do not get impacted during the development process. So as the dependencies get larger, it will become a costly affair to process them. Also, the case is, that there could be multiple formats for dependencies, such as ESM or CommonJS. But Vite provides pre-bundled dependencies with the help of esbuild. As the esbuild dependencies are all written in the Go language, it will become 10-100 times faster to process them as compared to the JavaScript-based bundlers.

Vite.js esbuild

Image Credits- telerik

vite.js- bundle based dev server

Image Credit- craftsmenltd

  • In general, the source code has non-plain JavaScript that needs to be transformed and edited but not to be loaded all simultaneously. Vite serves the source code over native ESM, allowing the browser to take over the bundler’s job. Vite transforms and serves source code only if there is a demand, as the browser demands it. It will only process the code behind conditional dynamic imports if needed on the current screen.

Vite.js Native ESM dev server

Image Credit- craftsmenltd

How does Vite deal with slow Updates?

Editing files in a bundler-based build setup can be inefficient because rebuilding the entire bundle takes time, and the update speed slows down as the app size increases. Some bundlers use in-memory bundling during development, which means they only need to update a portion of the module graph when a file changes. However, they still have to reconstruct the entire bundle and reload the web page, which can be expensive and resets the application’s current state.

To address these limitations, bundlers offer Hot Module Replacement (HMR), allowing modules to be replaced without affecting the rest of the page. While HMR improves the developer experience (DX), it also suffers from decreasing update speeds as the application size grows.

Vite takes a different approach by using native ESM for HMR. When a file is edited, Vite only needs to invalidate the chain between the modified module and its closest HMR boundary, usually just the module itself. This ensures consistently fast HMR updates, regardless of your application’s size.

Additionally, Vite leverages HTTP headers to accelerate full-page reloads. It offloads work to the browser by making source code module requests conditional using “304 Not Modified” responses, and dependency module requests are efficiently cached with “Cache-Control: max-age=31536000, immutable” to avoid unnecessary server hits.

After experiencing the speed of Vite, it’s unlikely that you’ll be willing to tolerate bundled development again. So, let’s take a look at what features are being offered by mighty “Vite”.

Key Features- Vite

Below are the features that are the core behind the fast working of Vite.

  • Speedy compilation and HMR

Vite leverages cutting-edge browser technology and native ES modules to compile your code instantly, resulting in speedy builds and immediate updates within the browser. By eliminating the requirement for a bundler during development, Vite drastically reduces the time spent on building and deploying applications. 

Additionally, Vite’s built-in development server is optimized for quick reloading and hot module replacement, enabling developers to witness real-time changes to their code without refreshing the entire page. Get ready for a seamless and efficient development experience with Vite!

  • Lazy loading

Vite uses lazy loading for modules, loading code only when needed. This reduces bundle sizes and boosts performance, especially for bigger apps. It also speeds up initial load times by loading non-critical code on demand.

  • Tree-shaking and code-splitting

Vite optimizes code size and performance by tree-shaking and code splitting. Tree-shaking removes unused code, while code splitting divides code into smaller, on-demand chunks. Users download only necessary code, leading to faster load times and improved performance.

  • Built-in Development server

Vite has a built-in development server designed for fast reloading and hot module replacement. It simplifies application development and testing by enabling real-time code changes without refreshing the entire page. The server also supports automatic code reloading for rapid iteration.

The latest version of Vite 4.0 was introduced last year with a huge ecosystem.

According to the Jamstack Community Survey 2022, Vite usage has surged from 14% to 32%, maintaining a 9.7 satisfaction score. Vite’s popularity is spreading among developers, with renowned frameworks embracing its magic.

Features of Vite 4.0 (latest version)

Below are the features-

  • Play with vite.new
  • New React Plugin Using SWC
  • Browser Compatibility
  • Importing CSS as a String
  • Environment Variables
  • Patch-Package Support
  • Reduced Package Size
  • Vite Core Upgrades
  • update @types/node to v18
  • patch-package support
  • support multiline values in env files

To read about them in detail, you can check out their official news site.

Advantages of Vite

Below is the list of unwavering benefits of using Vite for your next project development.

Vite.js Advantages

  • Improve the development workflow

Vite’s unique front-end development enhances the developer experience. It offers speedy builds, real-time browser updates, and a built-in server with hot module replacement. This improves workflow, reduces manual testing, and enables focused coding.

  • Faster build times

By embracing an ingenious strategy, Vite eradicates the necessity for a bundler during development, leading to swift builds and instantaneous browser updates. This translates to precious time saved for developers, especially when working on extensive projects, empowering them to channel their energy into crafting top-notch code that delivers excellence.

  • Optimized codes

With Vite’s ingenious lazy loading of modules and cutting-edge tree-shaking features, developers can now achieve optimized code sizes like never before. Say goodbye to bloated applications and welcome lightning-fast performance for your users. 

This dynamic duo of features empowers developers to effortlessly shrink the size of their code, unlocking a world of enhanced performance and unrivalled user experience. From colossal projects to intricate applications brimming with modules, Vite swoops in to save the day, revolutionizing the way we build software. 

  • Improved productivity

A game-changer for developers, unlocking faster build times, an enhanced development journey, and optimized code sizes. With Vite, your productivity soars as you effortlessly create, iterate, and refine your applications. Harness its potential to accelerate your time to market, paving the way for a streamlined and efficient development process. Say goodbye to delays and hello to high-quality applications delivered swiftly by your dynamic team.

  • Compatible with modern web standards

Vite is perfect for developers who want to use the latest front-end standards. It uses native ES modules and modern browser APIs, ensuring modern, scalable code. This minimizes future updates and simplifies application maintenance.

Disadvantages of Vite

While Vite offers numerous advantages, it’s crucial to contemplate its drawbacks before opting for it. Here are the key disadvantages of Vite.

  • Limited community support

Due to its recent development, Vite has a smaller user community compared to established tools like Create React App or Webpack, making support and problem-solving challenging to find.

  • Limited compatibility with browsers

Vite, where we harness the full potential of modern JavaScript features. These innovative functionalities, however, are still on the rise and not universally supported by all browsers just yet. Thus, it’s important to note that while most users will enjoy a seamless experience with your application, a small portion might need to update their browser or utilize a clever polyfill to unlock its full glory. 

Vite vs. Create React App

Vite vs. CRA

When considering Vite and Create React App (CRA), it’s valuable to draw comparisons between the two, as they do share certain similarities. Interestingly, one of the primary motivations that drive developers towards Vite is their desire to break free from the confines of CRA.

Create React App, a widely embraced frontend tool, is renowned for its ability to construct web applications utilizing the powerful React JavaScript library. It presents an exceptionally smooth and efficient pathway for commencing React development, furnishing developers with a user-friendly command-line interface for effortlessly creating and managing projects. Moreover, it boasts a development server that facilitates live reloading, making the development process all the more seamless and dynamic.

Vite vs. CRA comparision table

Today, many companies are thinking of migrating their apps from Create React App to Vite due to the above benefits. 

How to Migrate CR apps to Vite?

Moving an existing React project to Vote can be a complex process. To avoid confusion, I’ll break down the steps. Start by deleting the node_modules folder to configure the package.json file for Vite. 

  • react-script dependencies from the  package.json file and add vite. Make sure to use the latest version of Vite.

Vite.js latest version

  • Then run npm install or yarm then change the scripts within the package.json file.

Replace scripts in package.json.

  • Always remember to Move public/index.html to index.html for Vite to work. Make changes to the index.html file as shown below.

remove all the %PUBLIC_URL% from index.html:

  • Now add an entry point in the above file.

Add entry point in index.html:

In case you are working with typescript, add a typescript entry point. 

  • Now, create a vite.config.js or vite.config.ts file at the project’s root.

create a vite.config.js or vite.config.ts file

For small projects, the above step works fine, but in the case of big projects, you need to follow the following steps also.

  • Manage env variables.

Below is the .env file.

manage dependencies

  • By default inVite, environment variables begin with “VITE_”. Therefore, any variables starting with “REACT_APP_” should be substituted with “VITE_”.

env_ex

  • Save your time in replacing all env variables, you can simply use a plugin “vite-plugin-env-compatible”. You must run the following.

npm i vite-plugin-env-compatible or yarn add vite-plugin-env-compatible.

Then add the following code to vite.config.js or vite.config.ts file.

vite-plugin-env-compatible

  • The envPrefix in Vite specifies the beginning of each environment variable, eliminating the need to modify their names. However, process.env should be replaced with import.meta.env, which can be done easily by searching and replacing in VSCode.

envPrefix

  • Manage additional configs

To enable TypeScript path aliases in Vite, install “vite-tsconfig-paths” and add it to your Vite config.

vite-tsconfig-paths

  • If you are using the “aws-amplify package” in your project for cognito authentication then you need to make changes to the vite config file and add “aws-amplify”.

aws-amplify

  • To enable TypeScript path aliases in Vite, install “vite-tsconfig-paths” and add it to your Vite config.

index.html file

  • Then add the below code to the index.html file within the script tag, as shown below.

edit index.html file

It allows Global to work anywhere within the project.

  • Finally, if you’re utilizing SVG files in your project, you can import them as React components in React using “import { ReactComponent as logo } from ‘your-svg-path””. However, this will cause an error in Vite. To resolve this, install “vite-plugin-svgr” via npm or yarn and add it to your Vite configuration file.

your-svg-path

To begin, navigate to your src folder and modify the vite-env.d.ts file by adding the following line: /// <reference types=”vite-plugin-svgr/client” /> (Remember to include the ///). Following this, the import {ReactComponent as logo} from ‘your-svg-path’ will function correctly.

This is the procedure for transitioning your existing project to Vite. Although it may require some time and exploration, once you have made the switch, you will significantly reduce the duration of your development server runs and file builds for the production server. 

You will be amazed to see the performance difference.

Build time using CRA:

CRA build time

Image Credits- semaphoreci

Build time using Vite:

Vite.js build time

Image Credits- semaphoreci

Seeing these amazing results, many companies are switching to Vite. 

Companies that have Migrated to Vite

Below are the companies that have leveraged the power of Vite for improved performance.

  • Replit: Replaced CRA with react starter templates to utilize Vite and enhance user experience.
  • Cypress: Incorporating Vite into their products.
  • Storybook: Starting Storybook 6.3 with Vite as a viable option for building Storybook.
  • Tailwind: Recognized Vite’s potential early on and sponsored the project. Introduced Just-in-Time Mode, which pairs well with Vite HMR for an improved experience.
  • Astro: Reworked their engine to utilize Vite, abandoning Snowpack.
  • Glitch: Using Vite for their starter projects.

If you are a new company or developer, who wants to create a Vite app from scratch, then we can help you understand how it works with the most simple example.

Build your first Vite app from scratch

Before you jump straight to coding. Make sure to have all the minimum prerequisites to build an app.

Basic app requirements

Once you have all the latest versions compatible with Vite 4, then follow the below steps to get started. 

  • Build React app with default template values.

npm init vite@latest first-vite-react-app –template react

Then select React Framework and JavaScript as a variant. 

  • After creating a project, go to the project folder.

cd first-vite-react-app

  •  Now, install all dependencies and start the development server using the below command.

npm install

npm run dev

  • Then open the local url on the browser to check the app working. You must see the below screen.

Understanding Vite Project Structure

The project structure is somehow similar to “Create React App” with minimal configs.

folder structure

Image Source- dev.to

  • Public directory: Contains static assets (favicon, robot.txt, etc.)
  • index.html: Entry point for the application, treated as source code by Vite.
  • main.tsx: Main file for rendering App.jsx (similar to index.js in creating react app).
  • src directory: Contains project source code.
  • vite.config.js: Defines configuration options (base URL, build directory, etc.).
  • package.json: Lists project dependencies (package names with versions).

Build blazing-fast Vite App with OnGraph

Some might find it difficult to transition/migrate their existing apps to Vite and to create an app from scratch even with sufficient knowledge.

If you are just beginning then you must take help from experts who can help build potential solutions. We have a team of experts who are capable of smooth app migration and building completely new solutions for your business to scale.

Let’s schedule a call with OnGraph along with requirements and we will build a next-gen Vite app for you.