DEV Community: RuturajMaggirwar

Track changes in API Management using Azure Event Grids

RuturajMaggirwar — Mon, 30 Dec 2024 09:44:53 +0000

Introduction to Azure Event Grid

Azure Event Grid is a fully managed event routing service in Microsoft Azure that enables seamless integration between event producers and consumers. It supports high-throughput, low-latency communication and provides a reactive programming model to build scalable, event-driven architectures. At its core, Event Grid allows various Azure services, third-party systems, and custom applications to publish and consume events in a decoupled manner. In this blog, we are going to use these features to track changes that occur in API management to see new users that have been added and take any required actions on those new users.

How Azure Event Grid Works

Azure Event Grid is designed around the concept of event publishers, event handlers (subscribers), and topics:

Event Publishers: These are the sources of events. Examples include Azure resources like Blob Storage, Event Hubs, API Management, or custom applications.
Event Handlers (Subscribers): These are the endpoints or systems that respond to events. Examples include Azure Functions, Logic Apps, Webhooks, and third-party services.
Event Topics: Topics act as a hub where events are published. You can define custom topics or use system topics for Azure resources.

We are going to utilize Microsoft.ApiManagement.SubscriptionCreated event to track the new users created in API Management and extract key details from those users such as user email, user name, and user last name.

Azure Event Grid in Action

Azure Event Grid acts as the backbone for building reactive, event-driven architectures. When integrated with Azure API Management (APIM), it enables you to monitor key events and automate workflows that respond to changes in your API management system. Here's a detailed breakdown of how Event Grid works with API Management.

In the context of API Management, the event publisher is the API Management service itself. When specific actions occur within API Management (like the creation of a new subscription), API Management generates events and sends them to Event Grid.

Supported events include

Microsoft.ApiManagement.SubscriptionCreated: Triggered when a new subscription is created for an API.
Microsoft.ApiManagement.SubscriptionDeleted: Triggered when a subscription is deleted.
Microsoft.ApiManagement.UserCreated: Triggered when a new user is created in API Management.

Events like SubscriptionCreated are triggered whenever an action occurs in the API Management service. These events are sent to an Event Grid System Topic associated with the API Management instance.

Step-by-Step: API Management and Event Grid Workflow

Create an Azure function that will handle all the triggers from the Event Grid and fill in all the necessary details.

In the Azure function app, create a new Azure function which will have the code to fetch all the triggers from the Event Grid.

Configure API Management to Publish Events

Go to your API Management instance in the Azure Portal.
Navigate to Events under the Settings section.
Click + Event Subscription to create a subscription for Microsoft.ApiManagement.SubscriptionCreated event.

Fill all the details to create the Event Subscription

When creating the event subscription:

Event Type: Select Microsoft.ApiManagement.SubscriptionCreated.
Endpoint Type: Choose one of the following as the event handler:
Azure Function: Serverless logic to handle the event.
Logic App: Workflow automation.
Webhook: External endpoint for custom processing.
Select the Azure function that will receive all the events from the Event Grid and perform all the necessary actions based on the triggers.

After connecting the Azure Event Grid and the Azure Function App the integration should look like this:

Enter the code that will fetch the email from the new users that are added in the API Management:

module.exports = async function (context, eventGridEvent) {
    context.log('Received Event:', eventGridEvent);

    try {
        // Extract email from the event data
        const userEmail = eventGridEvent?.data?.user?.email;

        if (!userEmail) {
            throw new Error('Email not found in the event data.');
        }

        context.log('User Email:', userEmail);

        // Respond with the extracted email
        context.res = {
            status: 200,
            body: `User Email: ${userEmail}`,
        };
    } catch (error) {
        context.log('Error observed:', error.message);

        // Respond with error details
        context.res = {
            status: 500,
            body: 'Error observed: ' + error.message,
        };
    }
};

With this, the data from the new users will be fetched and new code can be added to this so that further processing can be carried out.

Key Advantages of Azure Event Grid

Decoupled Communication: Event publishers and subscribers don't need to know about each other, allowing independent evolution.
High Throughput and Low Latency: Event Grid supports millions of events per second with low latency, making it suitable for real-time scenarios.
Serverless Integration: Works seamlessly with Azure Functions, Logic Apps, and other serverless services, enabling quick development and deployment.
Flexible Routing: Supports advanced routing capabilities with filtering based on event properties.
Scalable: Automatically scales to handle large volumes of events.
Built-In Retry Mechanism: Ensures event delivery with retries and dead-lettering for failed deliveries.

Disadvantages of Azure Event Grid

Complex Configuration: For beginners, setting up custom topics, subscriptions, and event handlers may feel overwhelming.
Cost for High Volume: While cost-effective for moderate usage, handling large volumes of events can lead to higher costs.
Dependency on Azure Ecosystem: Though it supports external publishers and handlers, its tight integration with Azure services may limit multi-cloud strategies.
Event Size Limitation: The payload size for events is limited to 1 MB, which might be restrictive for some scenarios.

Other Use Cases of Azure Event Grid

Serverless Workflows: Trigger Azure Functions when new blobs are added to Azure Storage or when database records are updated.
Real-Time Notifications: Notify users of application updates or security events.
IoT Data Processing: Process events from IoT devices using Event Grid and route them to processing systems.
Monitoring and Auditing: Track resource changes (e.g., virtual machine creation) and log them for auditing purposes.

Conclusion

Azure Event Grid is a robust, event-driven solution that simplifies building scalable and reactive architectures. It decouples event producers and consumers, ensuring flexibility and scalability in modern cloud applications. While it has some limitations, its advantages—such as seamless serverless integration, real-time event processing, and high throughput—make it an excellent choice for many use cases.

Azure Event Grid simplifies event-driven automation with Azure API Management. This enables you to track key events like Microsoft.ApiManagement.SubscriptionCreated, Event Grid provides real-time visibility and actionable insights into subscription activities. Whether you're notifying users, updating databases, or triggering workflows, Event Grid ensures a scalable, reliable, and efficient event processing pipeline.

By leveraging the integration of API Management and Event Grid, you can create dynamic, automated workflows that improve API lifecycle management and enhance the developer experience.

Passing variables from the static page to the widget

RuturajMaggirwar — Mon, 23 Dec 2024 12:52:55 +0000

Introduction

Widgets are powerful ways to embed interactive functionality into static web pages. They allow developers to build modular components that can be reused across multiple websites, reducing redundancy and improving maintainability. One common scenario in widget implementation is the need to pass variables—such as configuration settings, API keys, or user-specific data—from the static page to the widget.

In this blog, we’ll explore how to securely pass variables from a static page to a widget, outline common use cases, and provide practical implementation examples.

Why Pass Variables from a Static Page to a Widget?

Imagine creating a reusable chat widget, analytics widget, or payment module. These widgets may require data such as:

A unique key or token for authentication.
User-specific IDs for personalization.
Random numbers for ensuring unpredictability or preventing cache collisions.

By passing variables dynamically, developers can:

Adapt widgets to different contexts.
Simplify widget reuse across multiple websites.
Reduce hard coding and enhance maintainability.

Implementation: Passing Variables Step-by-Step

Create the Static HTML Page - first, generate a random number dynamically using JavaScript and assign this number to a data attribute of the where the widget will render.
So modify the html file accordingly:

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Random Number Widget</title>
  <script>
    document.addEventListener("DOMContentLoaded", function () {
      const randomNumber = Math.floor(Math.random() * 1000);
      const widgetRoot = document.getElementById("widget-root");

      if (widgetRoot) {
        widgetRoot.setAttribute("data-random-number", randomNumber);
        console.log("Random number of the static page:", randomNumber);
      }
    });
  </script>
</head>
<body>
  <h1>Static Page with Random Number in Widget</h1>

  <!-- Div to hold the React widget -->
  <div id="widget-root"></div>

  <!-- Include the bundled React app -->
  <script defer="defer" src="bundle.min.js"></script>
</body>
</html>

In the React app, fetch the data-random-number attribute from the root element and change the code in the App.tsx file:

import { useEffect } from 'react';
import { Widget, addResponseMessage } from "react-chat-widget";
import "react-chat-widget/lib/styles.css";

function App() {
  useEffect(() => {
    const randomNumber = document.getElementById("widget-root")?
        .getAttribute("data-random-number");
    console.log("Random number from static page:", randomNumber);

    // Display the random number in the chat widget or use it as needed
    addResponseMessage(`Welcome! Your random number is: ${randomNumber}`);
  }, []);

  const handleNewUserMessage = (newMessage: string) => {
    console.log(`New message received: ${newMessage}`);
  };

  return (
    <Widget
      handleNewUserMessage={handleNewUserMessage}
      title="Random Number Widget"
      subtitle="Your session is unique!"
    />
  );
}

export default App;

Finally, bundle the Widget - Use Webpack to bundle the React widget into a bundle.min.js file, which can be included in the static page, and change the webpack.config.js file accordingly:

const path = require("path");

module.exports = {
  entry: path.join(__dirname, "src/index.tsx"),
  output: {
    path: path.join(__dirname, "dist"),
    filename: "bundle.min.js",
  },
  resolve: {
    extensions: [".ts", ".tsx", ".js"],
  },
  module: {
    rules: [
      {
        test: /\.ts(x?)$/,
        exclude: /node_modules/,
        use: "ts-loader",
      },
      {
        test: /\.css$/,
        use: ["style-loader", "css-loader"],
      },
    ],
  },
};

Build the widget using "npm run build".
Include the bundle.min.js file in your static HTML.
Open the static page in a browser and the React widget should display the random number passed from the static page.

Conclusion

Passing variables from static pages to widgets is essential for building flexible, dynamic, and modular components. By following the outlined steps, developers can ensure that their widgets integrate seamlessly into any environment while remaining secure and efficient. Widgets are a cornerstone of modern web development. With a little planning and care, they can be built to adapt to any context dynamically and securely.

Create embeddable widgets in react for static pages

RuturajMaggirwar — Mon, 30 Sep 2024 10:33:54 +0000

Introduction

In today’s digital world, embeddable widgets are everywhere, from chatbots and social media plugins to embedded video players and analytics dashboards. If you are a developer, you have likely come across the challenge of creating these embeddable widgets—especially if you want them to be easily integrated into static pages.

In this blog, we’ll explore what widgets are, how to create embeddable widgets using React for static pages, and discuss the advantages and disadvantages of these widgets. Let’s dive into the why and how of building embeddable widgets and explore a use case with an example.

What is an Embeddable Widget?

An embeddable widget is a self-contained block of functionality that can be dropped into any website, offering features like live chats, user feedback forms, or even media players. Widgets are often designed to work on various types of web pages and can be easily shared, reused, and customized for different webpages.

Example Use Cases:

Chatbots on e-commerce websites to guide users through purchases.
Social Media Feed widgets to display Twitter or Instagram posts.
Survey/Feedback Forms for collecting user input.
Analytics Widgets to display real-time metrics on a webpage.

Why Use React for Embeddable Widgets?

React, a popular JavaScript library for building user interfaces, is an excellent choice for creating embeddable widgets. It allows you to build encapsulated components that can manage their own state, which makes it easy to integrate complex functionality into any webpage. Additionally, React's component-based architecture simplifies the process of updating and maintaining widgets.

Advantages of Using React for Widgets

Component-Based: React’s modular structure allows for creating reusable components, ideal for embeddable widgets.
State Management: React makes it easy to manage dynamic content and interactions within widgets.
Performance: React is highly efficient, ensuring that even complex widgets perform well on any webpage.
Ease of Integration: React apps can be easily integrated into static HTML pages using Webpack or directly embedding the React app script.

Disadvantages of Using React for Widgets

Bundle Size: React’s ecosystem comes with libraries and tools that may increase the final bundle size of your widget, which can be a problem for performance-sensitive pages.
Learning Curve: React has a steeper learning curve compared to vanilla JavaScript or simpler frameworks. If you're new to React, it may take some time to get used to it.
Dependencies: You often need to use additional tools like Webpack or Babel to bundle and compile your code, adding some complexity.

Advantages and Disadvantages of Embeddable Widgets

Advantages:

Reuse: Widgets are reusable components that can be easily embedded into multiple web pages.
Modularity: Widgets encapsulate functionality, making them easy to update and maintain without affecting the host page.
Customization: You can easily pass variables from the host page to customize the widget’s behavior.
Cross-Site Deployment: Widgets can be deployed across different sites or applications, promoting code reuse.

Disadvantages:

Bundle Size: The more complex the widget, the larger the JavaScript bundle, which can impact performance.
Security: If not handled carefully, embedding widgets could expose sensitive data or make the host page vulnerable to attacks (like cross-site scripting).
Browser Compatibility: Older browsers may not fully support the latest JavaScript features or React components, requiring polyfills or additional configurations.

Step-by-Step approach

Let’s walk through a simple example of building a chatbot widget using React and embedding it into a static HTML page.

First we will set up a React App from scratch.

npx create-react-app embeddable-widget --template typescript
cd embeddable-widget
npm install react-chat-widget

Next we will create the Widget Component. In your App.tsx file, create a simple widget component using the react-chat-widget library:

import { useEffect } from 'react';
import { Widget, addResponseMessage } from "react-chat-widget";
import "react-chat-widget/lib/styles.css";

function App() {
  useEffect(() => {
    addResponseMessage("Welcome to our chatbot!");
  }, []);

  const handleNewUserMessage = (newMessage: string) => {
  };

  return (
    <Widget
      handleNewUserMessage={handleNewUserMessage}
      title="Chatbot Widget"
      subtitle="How can we help you?"
    />
  );
}

export default App;

Later we will configure Webpack for the Bundle. To make this React app embeddable, we need to bundle it using Webpack. The Webpack config will bundle the React app into a single widget.bundle.js file, which can be included in any static HTML page. Here’s a basic Webpack configuration:

const path = require("path");
const TerserPlugin = require("terser-webpack-plugin");

module.exports = {
    entry: path.join(__dirname, "src/index.tsx"),
    output: {
        path: path.join(__dirname, "dist"),
        filename: "bundle.min.js",
    },
    resolve: {
        extensions: [".ts", ".tsx", ".js"],
    },
    module: {
        rules: [
            {
                test: /\.css$/,
                use: ["style-loader", "css-loader"],
            },
            {
                test: /\.ts(x?)$/,
                exclude: /node_modules/,
                use: ["ts-loader"],
            },
        ],
    },
    optimization: {
        minimize: true,
        minimizer: [new TerserPlugin()],
    },
};

Next modify the index.tsx file accordingly:

import React from 'react';
import ReactDOM from 'react-dom/client';
import './index.css';
import App from './App';
import reportWebVitals from './reportWebVitals';

const root = ReactDOM.createRoot(
  document.getElementById('root') as HTMLElement
);

root.render(
    <App />
  );

reportWebVitals();

Finally we shall embed the Widget in a Static html Page. By linking the bundled JavaScript file, the widget will automatically render in the #widget-root div when the page loads. To embed the widget on a static HTML page, you need to include the generated bundle like so:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Document</title>
</head>
<body>
    <h1>New Static Page for testing embeddable widget</h1>
    <div id="root"></div>
    <script defer="defer" src="bundle.min.js"></script>
</body>
</html>

Conclusion

Embeddable widgets are an excellent way to add dynamic, reusable functionality to static pages. By leveraging React, you can build powerful, modular widgets that are easy to maintain and customize. However, it’s essential to consider bundle size, security, and the performance of your widget to ensure that it integrates smoothly with different web pages. By following the steps outlined in this guide, you should be well on your way to creating efficient, simple, and flexible embeddable widgets with React.

Langchain with Pinecone vs OpenAI Assistant

RuturajMaggirwar — Mon, 24 Jun 2024 10:23:31 +0000

Introduction

The world of conversational AI and natural language processing (NLP) has seen remarkable advancements with tools like Langchain and Pinecone emerging as innovative solutions. Meanwhile, OpenAI's Assistant remains a powerful and popular choice for generating human-like responses. In this blog, we will explore the differences between using Langchain combined with Pinecone and using OpenAI Assistant for generating responses. Understanding these differences will help developers and organizations make informed decisions based on their specific needs and constraints.

Langchain with Pinecone

Langchain is a scalable language technology that leverages blockchain to enhance data privacy, ownership, and collaborative learning. Pinecone, on the other hand, is a vector database designed for fast and scalable similarity search and retrieval. When combined, Langchain and Pinecone offer a unique approach to generating responses by fetching data from the vector database.

OpenAI Assistant

OpenAI Assistant is a robust AI model built on the GPT architecture, designed to understand and generate human-like text. It is widely used for various applications, from customer support to creative writing due to its powerful language generation capabilities. OpenAI Assistant provides excellent pre-trained language generation capabilities, making it ideal for content creation.

Key Differences

1. Data Privacy and Ownership
Langchain ensures that users retain ownership and control over their data. Data is securely encrypted and stored across a decentralized network, reducing the risk of unauthorized access. OpenAI Assistant operates on a centralized infrastructure where user data is processed and stored. While OpenAI has robust security measures, data control is limited to the organization.

2. Collaboration and Scalability
Langchain enables multiple parties to collaboratively train and improve language models without sharing sensitive data, fostering innovation while maintaining privacy. Pinecone provides scalable vector search capabilities, making it efficient to handle large datasets and complex queries in real-time. OpenAI Assistant is trained on vast datasets by OpenAI, and while it benefits from extensive training, collaboration at the data level is not inherently part of the framework.

3. Response Generation and Quality
Pinecone excels at similarity search, enabling Langchain to retrieve highly relevant information from large datasets quickly. OpenAI Assistant is known for its high-quality, human-like text generation, thanks to extensive pre-training on diverse datasets. The model provides consistent and coherent responses, making it reliable for various applications without additional customization.

4. Integration and Usability
Combining Langchain with Pinecone requires integrating blockchain technology with a vector database, which can be complex and may require specialized knowledge. However it does offers more flexibility for developers who want to customize their models and data handling processes. OpenAI Assistant is user-friendly and straightforward to integrate via API, making it accessible for developers without specialized expertise. It can be quickly deployed across a wide range of applications with minimal setup.

When it comes to language processing and generating responses based on custom data, both Langchain combined with Pinecone and OpenAI Assistant offer distinct advantages and disadvantages. Here’s a detailed comparison to help you understand the trade-offs of each approach.

Langchain with Pinecone

Advantages:

Users retain full control over their data as Langchain enables data to be stored in a decentralized and encrypted manner.
Decentralization reduces the risk of data breaches and unauthorized access, ensuring higher security for sensitive information.
Langchain allows for the creation and training of models on specific datasets, leading to highly customized and relevant responses.
Developers have the flexibility to integrate various components and fine-tune the system according to their specific needs.
Multiple parties can contribute to and improve models collaboratively without sharing raw data, preserving privacy while enhancing model performance.
Langchain supports multilingual capabilities, enabling the creation of applications that can understand and generate text in multiple languages.

Disadvantages:

Integrating Langchain with Pinecone requires significant technical expertise in blockchain technology and vector databases, making it challenging for developers with limited experience.
Maintaining a decentralized system and ensuring seamless operation can be resource-intensive.
The decentralized nature of blockchain can lead to scalability issues, such as slower transaction speeds and higher costs as the network grows.

OpenAI Assistant

Advantages:

OpenAI Assistant provides a straightforward API that is easy to integrate, allowing developers to quickly deploy the model in various applications.
The system requires minimal configuration and setup, making it accessible to developers without specialized knowledge.
The model is extensively pre-trained on a diverse dataset, leading to high-quality, coherent and human-like responses.
Provides consistent and reliable performance across a wide range of applications.
OpenAI’s infrastructure is highly scalable, capable of handling large volumes of requests with low latency and high reliability.
Suitable for a wide range of applications, from customer support to creative writing, without the need for significant customization.

Disadvantages:

User data is processed and stored centrally by OpenAI, which may raise privacy concerns and limit user control over their data. Users must trust OpenAI to handle their data responsibly, which can be a drawback for privacy-sensitive applications.
While OpenAI Assistant performs well out-of-the-box, it offers limited customization options compared to building and training models with Langchain.
Fine-tuning the model for specific use cases can be costly and requires access to sufficient computational resources.
There may be rate limits and quotas that restrict the number of requests, potentially impacting scalability for large-scale applications.

Choosing between Langchain combined with Pinecone and OpenAI Assistant largely depends on the specific requirements of your application. Here's a detailed analysis of which applications each technology is better suited for and why:

Applications Suited for Langchain with Pinecone:

Privacy-Sensitive Applications:
Healthcare, Finance and Legal Services. Pinecone's decentralized and encrypted data storage ensures that sensitive information remains secure and under user control, addressing privacy and regulatory compliance concerns.
Custom and Specialized Models:
Industry-Specific Chatbots, Research and Academia. Developing custom models for specific research projects or academic purposes, where data privacy and customization are crucial. Langchain allows for extensive customization and training of models on specific datasets, making it ideal for applications requiring tailored solutions.
Collaborative Projects:
Collaborative research initiatives and Decentralized Learning Projects. These are ideal applications as Langchain’s collaborative learning capabilities enable multiple parties to improve models without compromising data privacy, facilitating open innovation and collaboration.
Multilingual and Global Applications:
International Customer Support and Global Market Analysis. Langchain’s multilingual capabilities ensure that models can understand and generate text in various languages, making it suitable for global communication applications.

Applications Suited for OpenAI Assistant:

General-Purpose Chatbots:
Providing automated customer support for e-commerce, telecom, and other industries. Virtual Assistants that help with scheduling, reminders, and basic inquiries. OpenAI Assistant offers high-quality, human-like responses out-of-the-box, making it ideal for general-purpose chatbots that need quick deployment and consistent performance.
Content Creation and Creative Writing:
Blogging and Article Writing can help authors and scriptwriters with creative writing tasks. OpenAI Assistant’s advanced language generation capabilities are excellent for producing coherent and creative text, supporting content creation and writing tasks.
Educational Tools and E-Learning:
Interactive Tutors and Language Learning can assist learners with language practice, vocabulary building, and grammar correction. OpenAI Assistant’s ability to generate informative and contextually relevant responses makes it suitable for educational applications and e-learning platforms.
Marketing and Customer Engagement:
Social Media Management can be used for automating responses to customer queries and engaging with followers on social media platforms. It can generate engaging and contextually appropriate content, making it effective for marketing and customer engagement activities.
Automation of Routine Tasks:
Data Entry, Analysis and Technical Support can be provided for troubleshooting of software and hardware products. OpenAI Assistant’s reliable and consistent performance is well-suited for automating routine and repetitive tasks, improving efficiency and reducing manual workload.

Conclusion

Langchain with Pinecone is best suited for applications where data privacy, ownership, customization, and collaborative learning are critical. It excels in privacy-sensitive industries, specialized models, collaborative projects and multilingual applications. However, it does come with higher integration complexity and requires a more hands-on approach.

OpenAI Assistant is ideal for general-purpose applications that require high-quality, human-like responses with minimal setup. It is perfect for customer service, content creation, educational tools, marketing and automation of routine tasks. OpenAI Assistant has some drawbacks as well including additional token costs and limited customization.

Selecting the right technology depends on your specific needs, including privacy requirements, the level of customization needed, collaboration scope, language diversity, scalability and cost, and ease of integration. Both Langchain with Pinecone and OpenAI Assistant offer unique strengths that can be leveraged to build effective and efficient AI-driven applications.

Unveiling mysteries of the Code: Power of Documentation

RuturajMaggirwar — Mon, 18 Mar 2024 13:02:30 +0000

Introduction

A vital but frequently disregarded component exists in the broad field of software development, where logic and creativity coexist: documentation. Documentation is a guide for developers that helps them in navigating the complexities of their creations, separating the fundamentals of code into written form. It includes everything from comments made directly in the code to detailed instructions and manuals explaining the features, application, and implementation specifics of software systems. Programming is a community that benefits greatly from the understanding, cooperation, and longevity that documentation provides.

Come along with us as we set out to explore the importance of documentation and its profound influence on the software development industry in a unique and fun manner.

The Story Begins

Once upon a time, in the realm of programming, there existed a kingdom of code. Within this kingdom, developers roamed freely, crafting magnificent creations that breathed life into the digital world. But amidst the lines of syntax and algorithms, there was a silent hero, often overlooked yet indispensable – documentation.

Our journey begins with a young developer named Alice. With a heart full of ambition and a mind brimming with ideas, she embarked on a quest to build her own software kingdom. Armed with her favorite programming language and a thirst for knowledge, she dove headfirst into her project.

In the beginning stages of her project, Alice experienced a sense of exhilaration as lines of code danced gracefully from her fingertips. Each function and variable she crafted seemed to fit seamlessly into the grand environment of her software. With a clear vision and a steady hand, she built her creation with confidence, fueled by the excitement of seeing her ideas come to life.

However, as time passed and her project expanded in scope and complexity, Alice encountered a formidable adversary: the tangled web of confusion. Suddenly, the once-clear pathways through her code became obscured by a dense array of functions, algorithms, and dependencies. She found herself grappling with questions that seemed to have no easy answers. What were the version of the different dependencies she was using? What was the purpose of that seemingly obscure function she had written weeks ago? How did this intricate algorithm she had devised months ago actually work?

As Alice delved deeper into her codebase, she realized that her initial confidence had given way to uncertainty and doubt. The very complexity that had once excited her now threatened to overwhelm her. Without proper documentation to guide her, she felt adrift in a sea of code, struggling to make sense of the intricate machinery she had built.

Feeling lost and frustrated, Alice decided to take break to relieve herself from all the stress and exhaustion. While strolling across the gGarden of Wisdom, she stumbled upon a sacred tome known as "The Art of Documentation." Intrigued, she looked into the tome and found a book covered in a mountain of dust. Unable to control her fascination, she picked it up and delved into its pages, discovering the ancient wisdom hidden within.

It was in this moment of uncertainty that Alice recognized the importance of documentation. Like a beacon cutting through the fog. With newfound clarity, she began to document her code diligently, weaving a narrative that would guide her and others through the labyrinth of her creations.

As she continued her journey, Alice soon realized the myriad benefits of documentation. Like a map leading through uncharted territory, her comments and explanations provided guidance to fellow travelers who dared to venture into her codebase. No longer were her functions mysterious enigmas; they were now well-documented allies, ready to aid anyone who sought their assistance.

But the benefits of documentation did not end there. In her quest to explain her code to others, Alice found herself gaining a deeper understanding of her own creations. Concepts that once seemed abstract became clear as she translated them into human-readable prose. And when the time came to make changes or additions, her documentation served as a roadmap, guiding her through the twisty curves of her codebase with ease.

Yet, like any tool, documentation had its drawbacks. Some argued that it slowed the pace of development, insisting that time spent writing comments could be better spent writing code. Others found themselves struggling to maintain consistency in their documentation, leading to confusion and frustration among those who relied upon it.

However, Alice knew that these challenges were but small hurdles in the grand scheme of things. With discipline and practice, she found ways to integrate documentation seamlessly into her workflow, ensuring that it enhanced rather than hindered her progress. Over a period of time, some saw the progress Alice had made with her code and decided to collaborate with her.

Documentation served as a valuable tool for knowledge transfer. As new team members joined her project or as she collaborated with developers across different teams, Alice found that well-documented code provided a smooth onboarding experience. Newcomers could quickly familiarize themselves with the project's architecture, design decisions, and best practices, reducing ramp-up time and facilitating a seamless transition into their roles.

Furthermore, documentation acted as a safeguard against knowledge loss. In the fast-paced world of software development, team members may come and go, but well-documented code endures. By capturing crucial insights, assumptions, and decisions within her documentation, Alice ensured that the collective wisdom of her team remained intact, even as individual members moved on to new challenges.

With hard work, practice and the wisdom of documentation, she was able to convince all her fellow coders the power of documentation, changing the course of coding in the realm of programming forever.

Conclusion

And so, dear reader, the tale of Alice and the power of documentation comes to a close. In the ever-evolving landscape of programming, documentation remains a timeless ally, offering guidance, clarity, and understanding to those who dare to wield its power. So, the next time you embark on a coding adventure of your own, remember the lessons of Alice and the importance of documenting your journey.

Introduction to LangChain

RuturajMaggirwar — Thu, 14 Dec 2023 13:00:50 +0000

Introduction

In the rapidly evolving technological landscape, language plays a critical role in facilitating communication between humans and robots. These AI-powered language models have greatly advanced the field of natural language processing (NLP). LangChain is the idea that is most revolutionary in this field. One of the most exciting developments in technology is LangChain, which seeks to push language processing to unprecedented levels. In this blog, we'll go over the meaning and workings of LangChain and witness the advantages and flaws it holds.

What is LangChain ?

One of the most effective frameworks for developers wishing to create applications with LLM capabilities is LangChain. It makes it easier to organize enormous amounts of data so that LLMs can access it quickly and enables LLM models to generate responses based on the most recent data that is available online.

By building a bridge between different language models and online data sources, LangChain enables the development of extremely complex applications. LangChain provides the connecting pieces that are needed between the individual pieces, for instance, when building a Lego structure. Developers are empowered to create intelligent applications with LangChain because it seamlessly integrates language processing capabilities with a wide variety of data.

How does LangChain Work?

In LangChain, linking different models forms a chain. These chains link to various applications to produce a special integration of language models and various data sources, improving natural language understanding and providing enhanced user experiences.

Even when used with only one prompt, LLMs are already very powerful. But by supposing the most likely word to come, they effectively carry out completions. They don't pause to consider their actions or their responses the way humans do.

The process of drawing new conclusions from data obtained before the communication act is known as reasoning. With the LangChain framework, programmers can design agents that can break down larger tasks into smaller ones and reason about them. With LangChain, you can use intermediate stages to add context and memory to completions by chaining together complex instructions.

Example of how LangChain works:

Consider a scenario where a business wants to understand customers' sentiments across various product categories based on online reviews by generating a SQL query.

Without LangChain, the developer would have to manually identify relevant tables and schema for sentiment analysis and then construct an SQL query. The process is time-consuming, requiring manual identification and query formulation.

But by using LangChain, developers can instruct the LLM to identify tables and schema related to customer reviews and sentiment analysis. The LLM, through LangChain, autonomously navigates the process, uncovering relevant information. LangChain streamlines the development process by allowing developers to focus on guiding the model through high-level instructions, automating the exploration of data structures and generating responses efficiently.

Benefits of LangChain

Scalability: Applications that can process enormous amounts of data can be developed using LangChain.
Adaptability: A broad range of applications can be developed using the framework thanks to its adaptability.
Extensibility: The framework's expandability allows developers to incorporate their own features and functionalities.
Simple to use: To facilitate the development of complex applications, LangChain provides a high-level API for linking language models to diverse data sources.
Open source: LangChain is a freely available framework that can be used and altered.
Vibrant community: You can get help and support from a sizable and vibrant community of LangChain developers and users.
Excellent documentation: The documentation is clear and comprehensive.
Integrations: Flask and TensorFlow are just two examples of the libraries and frameworks with which LangChain can be integrated with.

Drawbacks of LangChain

Varying Actions and Confidential Information: LangChain has come under fire for acting inconsistently and concealing crucial information, which can cause problems in live systems.
Absence of a consistent standard Datatype: The lack of a common data representation format in LangChain is another disadvantage. This lack of consistency can make it difficult to integrate with other frameworks and tools, which makes it difficult to operate in a larger machine learning tool ecosystem.
Superior Substitutes: Better options are available that provide more functionality, activity, scalability and ease of use.

Conclusion

LangChain is a catalyst for the next step in language technology development, not just a framework. There are numerous advantages to using LangChain. Its versatility permits a wide range of use cases and its scalability supports applications handling large datasets. Like any innovative technology, LangChain is not without its difficulties. Different actions, information hiding, and the lack of a standard datatype that is compatible have all been brought up for concern.

To sum up, LangChain marks a turning point in the development of language-driven applications. Its capacity to combine a variety of data sources and language models paves the way for the development of intelligent applications that will smoothly combine the online knowledge with human-like language comprehension in the future. As LangChain continues to evolve, its impact on the landscape of AI and language technology promises to be transformative.

ChatGPT vs GitHub Co-Pilot: A Comparative Analysis

RuturajMaggirwar — Mon, 11 Dec 2023 12:39:24 +0000

Introduction

Artificial intelligence has come a long way in the last few years, and this has resulted in the creation of strong language models with useful applications across a range of industries. Two of OpenAI's most prominent inventions, ChatGPT and GitHub Copilot, have drawn notice for their prowess in natural language analysis. They are, nevertheless, tailored for various situations and fulfill diverse functions. We will compare and contrast them in this blog article to see which would work better in certain situations.

What is ChatGPT?

ChatGPT is a flexible language model that is based on the GPT (Generative Pre-trained Transformer) architecture. It can respond to questions from the user by producing text based on prediction and having conversations in natural language.

What is Github Co-Pilot?

Github Co-Pilot, powered by OpenAI's Codexis, is an innovative tool designed specifically for engineers. It works effortlessly with well-known integrated development environments (IDEs), helping programmers by providing code ideas as they type, significantly expediting the coding process.

How does ChatGPT and Github Co-Pilot work?

We must delve into these two creations' inner workings in order to analyze them more thoroughly.

The OpenAI-developed GPT architecture serves as the basis for ChatGPT. An extensive amount of internet text is included in a diversified dataset that the model was pre-trained on. By anticipating a sentence's next word, the model is trained to understand syntax, context, and the semantic connections between words. After pre-training, the model can be improved for specialized applications by fine-tuning it on certain tasks or domains. The trained model uses input prompts from users to create responses during inference. It makes use of the acquired patterns to forecast and produce response in real-time.

GitHub Copilot makes use of the capabilities of OpenAI's Codex language model to offer intelligent code completions and recommendations. Additionally, Codex is trained on an extensive dataset that comprises a sizable portion of publicly accessible code from numerous online sources. It can comprehend a broad variety of programming patterns and structures thanks to its considerable training. GitHub Copilot evaluates the context—variable names, function names, and the general structure as developers type code. Then, in real-time, it proposes pertinent and contextually suitable code snippets.

Comparing the Approaches:

ChatGPT can be integrated into a variety of conversational interfaces and is primarily intended for use in natural language applications. GitHub Copilot is integrated into well-known IDEs and is designed specifically for coding. While GitHub Copilot generates code snippets based on the coding context, ChatGPT produces text responses that resemble human speech.

Knowing these underlying mechanisms helps explain why ChatGPT performs well in natural language tasks and why GitHub Copilot is designed specifically to help developers with intelligent coding.

Benefits of ChatGPT and Github Co-Pilot

Advantages of Github Co-Pilot:

GitHub Copilot produces code snippets more quickly than any other tool.
Because it speeds up the coding process so much, it's perfect for tasks involving boilerplate or repetitive code.
Copilot is flexible and available to a wide spectrum of developers due to its support for a large number of programming languages.
The load on developers is lessened by Copilot's ability to suggest entire lines or blocks of code.
Over time, Copilot adjusts to the coding patterns of the developer, providing tailored and contextually appropriate recommendations.

Advantages of ChatGPT:

Because ChatGPT is so good at comprehending and producing natural language responses, it can be used for a variety of conversational applications.
ChatGPT is flexible and can be used for a wide range of tasks other than coding, such as creating content, coming up with ideas, and responding to inquiries.
Because of its adaptability, it can be used in a variety of contexts involving the creation and understanding of language.
Unlike Copilot, ChatGPT can be used in non-technical contexts because it is not limited to programming languages.

Drawbacks of ChatGPT and Github Co-Pilot

Disadvantages of Github Co-Pilot:

Depending too heavily on Copilot suggestions may lead to a diminished understanding of code logic and hinder the development of essential coding skills.
Copilot's suggestions may lack context awareness, leading to occasional inaccuracies in code suggestions.
Developers need to review and validate Copilot-generated code to ensure correctness and security.

Disadvantages of ChatGPT:

ChatGPT is not explicitly trained for code-related tasks, making it less suitable for programming knowledge.
It may not be the optimal choice for developers seeking precise code-related assistance.
In certain situations, ChatGPT may generate ambiguous or nonspecific responses, especially when dealing with complex or technical queries.

Conclusion

In selecting between ChatGPT and GitHub Copilot, users must consider the specific needs of their tasks. ChatGPT excels in natural language understanding, making it ideal for use cases such as content generation, conversational agents, question answering and tutoring with education.

On the other hand, GitHub Copilot is a coding powerhouse, making it ideal for tasks like code generation and completion, rapid prototyping and learning coding patterns. All these provide invaluable assistance to developers by generating code snippets and enhancing productivity in coding tasks.

The choice between GitHub Copilot and ChatGPT hinges on the specific use case and requirements. Users should carefully evaluate their needs, considering the advantages and disadvantages outlined, to determine which tool aligns better with their goals.

Choose GitHub Copilot If:

You are a developer looking to boost coding productivity.
Your primary focus is on coding tasks and generating code snippets.

Choose ChatGPT If:

Your application involves natural language understanding and generation.
You need assistance with a broad range of non-programming or programming tasks, such as content creation or conversation.

As the field of AI continues to advance, both tools showcase the potential of language models in enhancing productivity and interaction in different domains.

So choose wisely ...

Integrating ChatGPT APIs into apps and games

RuturajMaggirwar — Thu, 15 Jun 2023 07:19:58 +0000

Introduction:

Interactive experiences are becoming a more crucial component of user engagement in the rapidly changing world of technology. The incorporation of chatbot features into many applications, including games, is one new trend. Game and app developers can improve user experiences and create impressive virtual worlds by integrating conversational bots powered by artificial intelligence, such as ChatGPT APIs.

We will examine the advantages and potential uses of integrating ChatGPT API into games and apps in this blog article.

Let us look at ways in which ChatGPT can enhance the user experience and how it offers several capabilities that can be integrated into apps or games. Here are some ways you can leverage the ChatGPT API:

Conversational Agents: You may build interactive, dynamic conversational agents for your app or game using the ChatGPT API, creating interactions that are realistic and exciting.
Virtual assistants: Include ChatGPT API capabilities in your app or game to offer the users a virtual assistant that allows them to request information and receive helpful guidance.
Use the ChatGPT API to develop intelligent non-player characters (NPCs) in video games who can interact meaningfully and offer advice or hints.
Use the ChatGPT API to enable interactive storytelling in your app or game. Individuals can actively participate in the conversation making the user experience more real.
Language Learning and Translation: Users can interact with players or characters who speak multiple languages, and the AI can provide real-time language translation.
Utilise the ChatGPT API to provide dynamic and customized content for your app or game.

Benefits of this integration as well as drawbacks:

Integrating ChatGPT APIs into apps or games can bring various benefits, but it's important to consider potential drawbacks as well. Here are some pros and cons to consider:

Pros:

Improved user experience.
Users can take advantage of tailored interactions.
Better Engagement and Retention.
It enables developers to offload sophisticated AI capabilities to outside services, hence decreasing the processing load on the app or game.
Pre-trained models can be used through APIs to save time and money.
It guarantees that your app or game makes use of the most recent developments in conversational AI and natural language processing and hence stays up-to-date.

Cons:

Reliability and Dependency on the third-party APIs.
Privacy and data protection concerns to safeguard user data.
Although APIs provide robust capability, they may have restrictions on modification or particular use cases.
Some APIs, like the ChatGPT API, may charge a subscription fee or charges that are based on usage.
The inability to control the model's behaviour could lead to replies that are unreliable or biased.

How to integrate ChatGPT into apps / games

Integrating the ChatGPT API into apps involves a series of steps. Here's a general overview of the process:

Obtain API Access key:
The ChatGPT API must first be accessed. You can register for access and find out more about the API by going to the OpenAI website (https://www.openai.com). You will be given the relevant information, including an API key, once your application has been accepted.
Select the App Platform:
Select the platform on which the ChatGPT API will be used. It might be a desktop application, a web application, or an iOS or Android mobile app. The platform you select may have a little impact on the integration procedure.
Configure API Integration:
The API integration needs to be configured in your preferred app platform next. Installing the required OpenAI SDKs or libraries is usually required for this. These SDKs will make it easier for your app and the ChatGPT API to communicate.
Send the API:
Start sending API queries to communicate with the ChatGPT model after the integration has been configured. Send the user's message or input together with an API request payload to the ChatGPT API endpoint.
Manage API:
Your app must adequately manage and process any responses it receives from the ChatGPT API. Take the pertinent data from the answer, like the model's generated message, and incorporate it into your app's user interface
Implement User Experience:
Create and put into practise the user experience for your app's chatbot feature. Identify the layout, design, and user interaction patterns that should be used to present the user and AI's conversation. Think about how the discussion will progress and how the user will see the AI's responses.
Test and Iterate:
Carefully examine the integration's functionality and user experience to guarantee correct operation. Observe numerous situations, such as varied user inputs and probable edge cases. To enhance the performance and usefulness of your integration, get user input and make revisions.
Handle Edge Cases and Errors:
Put error-handling and fallback procedures in place to handle scenarios where the API might not work or might produce unexpected results. To ensure a smooth discourse, offer alternate suggestions or questions.
Constantly Update and Improve:
Monitor the AI character's performance and collect player input. In order to improve the conversational experience of the game over time, make adjustments based on user interactions and iterate on the integration.

To make sure you're following best practises and making use of any supplied resources or examples, keep in mind to adhere to the official OpenAI documentation and any platform-specific instructions.

Conclusion:

Exciting opportunities to improve user experiences and enable sophisticated conversational capabilities can be found by integrating APIs like the ChatGPT API into apps and games. Improved engagement, scalability, and time savings are a few advantages. However, it's critical to handle potential issues including reliance on outside services, privacy worries, restrictions on customisation, and the requirement for suitable content monitoring.

Developers have a wide range of options for creating more interactive, immersive, and customized experiences by integrating ChatGPT API into games. The incorporation of ChatGPT API into games is an exciting frontier for game makers, pushing the limits of what is possible in interactive entertainment at a time when technology is developing quickly.

Combining Custom Functions with Taskpane for office Add-ins

RuturajMaggirwar — Mon, 27 Mar 2023 12:16:28 +0000

Understanding an Add-in

An add-in is a software component or program that adds new features or functions to an existing software application. Add-ins are designed to extend the functionality of an application, such as a spreadsheet or word processing program, and typically provide additional capabilities beyond the core features provided by the software.

Creating an Add-in using Yeoman Generator

A Yeoman generators is an exceptional tool that defines a set of rules and templates for creating office add-ins. The Yeoman Generators are specific to a particular type of project or technology stack, such as a generator for a React web app or a generator for a Node.js API.

When you run a Yeoman generator, it automatically generates a set of files and folders that make up the project structure. This can include things like configuration files, sample code, and other assets. This can save developers a lot of time and effort by automating the setup and configuration of new projects.

The challenging part about this generator is combining the features of 2 or more projects. We will look into combining the custom functions feature with the Taskpane.

Let us look into what Custom Functions are

Custom functions in Excel, also known as user-defined functions, are functions that individual users can create which allows you to create your own formula or function in Excel. Using these custom functions, we can performs a specific calculation or task that is not already available in Excel.

With custom functions, we can perform complex calculations, manipulate text and automate repetitive tasks that are not possible with built-in functions. Custom functions can also be designed to work with other Excel functions, making it easier to create more powerful and flexible formulas.

To create a custom function in Excel, the Yeoman Generator automatically provides a pre-built project structure where the custom functions can be coded. This can save you time, improve the accuracy of your calculations, and make your spreadsheets more efficient and user-friendly.

So what does the Taskpane hold

In Microsoft Excel, a task pane is a feature that provides easy access to common actions and tools. The task pane is a panel that typically appears on the right side of the Excel window, and it can be opened by clicking on a button or menu item in the Excel ribbon.

The task pane can be used to perform a variety of tasks, such as inserting charts, tables, and PivotTables, formatting data, and adding custom calculations. It can also be used to access features that are specific to certain add-ins, such as third-party data analysis tools.

The task pane is designed to provide quick and convenient access to tools and options that are frequently used, making it a useful feature for both novice and experienced Excel users, depending on your needs and preferences.

Combining the 2 projects

Combining a task pane and a custom function in a software application can provide users with a powerful tool for performing complex tasks quickly and efficiently.

A task pane is a window that is typically displayed alongside the main application window, providing access to additional tools or features. Task panes can be used to display custom user interfaces, provide quick access to frequently used functions, or display additional information related to the current task. A custom function, on the other hand, is a user-defined function that extends the functionality of the application by performing a specific task or calculation that is not included in the built-in functions.

While using the Yeoman Generator, the pre-built files are different for a Custom Function project and a Taskpane project. Hence in order to combine a task pane and a custom function, you need to overlay a few features for both the projects to function properly.

Using the following steps we can overlap these files so that all the functionalities of custom functions and taskpane work in sync.

Create the project for excel add-in as a custom function project by using yeoman generator. It is easier to integrate features of taskpane with custom functions.
Add the dependencies and the dev-dependencies for the word plugin in the package.json file.
Remember to configure the version of the dependencies which are compatible for the word add-in.
Add the custom function plugin in the webpack.config file
Change the front end in taskpane.html file according to requirement so that you can change the default custom function front end that is displayed in taskpane.
Comment out the code which runs as the default in taskpane.ts but keep the Office.Onready() function working.
Add your custom functions in the functions.ts file.
Make sure these changes are configured or available in the manifest.xml file.

<Runtimes>
     <Runtime resid="Taskpane.Url" lifetime="long"/>
</Runtimes>

<Page>
     <SourceLocation resid="Taskpane.Url"/>
</Page>

In conclusion, combining custom functions and taskpane features can greatly enhance the functionality and user experience of Microsoft Office applications. Custom functions allow users to create their own formulas and automate complex tasks, while taskpane features provide a visual interface for users to interact with and manipulate data. By combining these features, users can create more efficient workflows and accomplish tasks, making it easier for them to accomplish their tasks and achieve their goals.

Introduction to Machine Learning on Microsoft Azure

RuturajMaggirwar — Sun, 25 Dec 2022 07:39:07 +0000

So, what is Machine learning?

Machine learning is embedded in our everyday activities. Machine learning is all about teaching a machine how to react based on data which it encounters. And as it continues to encounter this data, it must absorb that information and learn from it, like a child. Machine learning it rooted in a plethora of apps and software; from predicting the weather report to showing the best route out of the traffic to voice assistance.

This blog shares the basics of Machine Learning on Azure and why it is a platform which is recommended for performing data predictions.

Different Algorithms on Azure

Azure makes available a variety of different machine learning algorithms that can be utilized as per the application requirements. Typical machine learning algorithms fall under 3 types; supervised, unsupervised and reinforced. Azure has provision for all these types.

Supervised – this type of machine learning is used to predict the information based on already available data. The data is used to pre-train the model and the parameters between which the output should be, is known. It makes use of labelled data to predict the outcome.

Unsupervised – in this technique, no prior information has been provided to the model. This technique is useful when you don’t know what the outcome should look like cause the data is provided in an unlabeled format.

Reinforcement learning - this learning uses algorithms that learns from feedback that helps it determine if the choice the algorithm has made was correct, neutral, or incorrect. It’s a good technique to use for automated systems without human guidance.

In Azure machine learning, these 3 different algorithms are further divided into these following categories. These are different models that can be applied according to the use case:

Linear regression
Logistic regression
Naïve Bayes
Support Vector Machines (SVM)
Decision tree
K-Nearest neighbor
Random forest
K-Means

How it works on azure

The conventional flow of any machine learning algorithm is firstly pre-processing raw data in order to filter it by eliminating missing values, removing or reducing noisy data and checking other inconsistencies before executing it to the algorithm. The prepared data is then fed into the algorithm and models are generated. The ideal model is chosen which will be used for all other data sets.

But on Azure, there are several in-built resources that can be consumed to abate the efforts of these tasks.

Azure ML Studios is consumed for accelerating and managing the machine learning project lifecycle. It assists the consumer for pre-processing the data and carrying out the whole process efficiently. It benefits the users by minimizing the repetition of tasks and automates these tasks, carrying out bulk of the tasks which are repetitively.

Azure ML API is used to provide operations for managing datastores, environments, data assets and managing the code assets.

Benefits and drawbacks of Machine learning on Azure

Advantages:
• It increases productivity by accelerating the machine learning project lifecycle.
• Reproducibility also increases as Azure will handle the process to pre-process data, deploy and monitor the models.
• Reliability is improved as Azure cloud platform is secure.
• Connectivity makes it available for several users to work on the same application at the same time.
• Monitorability makes it easier to visualize the response and progress of the models.
• Low or no coding is involved, making it easier for non-technical people to get started with machine learning.
• Azure makes it easier to convert to production-ready with simply a click.

Disadvantages:
• Azure machine learning has several pre-trained models but not all so fewer algorithms and transformations are built-in.
• As Azure is a cloud platform, it will charge money for utilizing its resources.
• Running the models will consume more computing power and storage as the model builds results.
• Azure ML is a high-level tool, which therefore grants you less control over the model which is built.

Conclusion:

Azure provides several machine learning resources which can be utilized to benefit the user based on the application. There are a few cons but the pros outweigh them because these pros provide functionalities which will help the user to progress.

It is very good for working in a collaborative teams in which the team members with zero coding experience can view the same data. Azure provides industry leading machining operation which allows the models to be monitored after they have been deployed and trained. It can integrate with other open-source machine learning platforms such as tensor flow or scikit-learn. Moreover, the environment that is created to host the pipelines and operations are scalable, robust and secure.

Therefore, Azure machine learning is a great tool to develop machine learning application and utilize those resources to find data patterns, prediction and flaws.