How to use Visual Studio for ML.Net Model Builder Step By Step Guide

 

This post will explain, how we can Download, install and Configure Visual Studio for ML.Net Model Builder.

 Go to -  https://visualstudio.microsoft.com/downloads/

Click on Free download under Community

 

 

 

Right click on the .exe which is downloaded on your local system

Run as Administrator

Once it starts installing below screen will show

 

Under workloads – select different workloads for your requirement and for ML Model Builder we need to select .NET desktop development

We can select other workloads as well based on our requirement.

There are other workloads like Data science and analytical applications and Data storage and processing

Under this workload we need to select ML.NET Model Builder – If it is not selected

Check for Location, where you want to install and Total space is required.

After successful installation, open IDE and create new project and select appropriate template

And class library, as per below image shows, and I have selected here .NET or .NET Standard

 

 

After that, give the meaningful project name.

 

Select correct framework

Once everything is done, visual studio will open and looks like below.

 

Manage Data -> Train Data -> Evaluate Models -> Deploy Model

 Right Click on the Project -> Add-> Machine Learning Model

 

 

 

Once you click on Machine Learning Model, then below page will display and u can name to your model config, which has all the configurations for that model.

 

 

 

Once create that file, below image will display with different GUI feature for data in Model Builder.

There are different types of scenarios like – Data classification, Value prediction, Recommendation, Image classification.

You can select any one scenario, based on your requirement.

We will have different types of model builders in the market

Google Vertex AI (AutoML)

Microsoft Azure Machine Learning Studio

IBM Watson AutoAI

Lobe (by Microsoft)

Teachable Machine (by Google)

Spring AI , AI Model providers, ETL Data Engineering for AI Model

 

Spring AI is an application framework for AI engineering. 

Its goal is to apply to the AI domain Spring ecosystem design principles such as portability and modular design and promote using POJOs as the building blocks of an application to the AI domain.

AI Model providers

   Portable Model API across AI providers for Chat, Text to Image, Audio Transcription, 

   Text to Speech, and Embedding models.

    With support for AI Models from OpenAI, Microsoft, Amazon, Google, Amazon Bedrock, Hugging Face and more.

    

ETL Data Engineering

ETL framework for Data Engineering. This provides the basis of loading data into a vector database, helping implement the Retrieval Augmented Generation pattern that enables you to bring your data to the AI model to incorporate into its response.

How Git Hub Copilot Write Code for you

 

This post will explain,  how we can configure Copilot in IntelliJ and how we can generate code using Copilot.

To configure GitHub Copilot in IntelliJ IDE, follow these step-by-step instructions:

Step 1: Prerequisites

GitHub Account: Ensure you have an active GitHub account with a subscription to GitHub Copilot.

IntelliJ Version: Update IntelliJ IDEA to the latest version for compatibility (2021.3 or later is recommended).

JetBrains Plugin Repository Access: Verify that your IntelliJ IDE is configured to access JetBrains plugins.

Step 2: Install the GitHub Copilot Plugin

  Open IntelliJ IDEA:

1. Launch IntelliJ IDEA on your system.

2. Access the Plugin Manager:

   Go to File > Settings (or Preferences on macOS).

     Navigate to Plugins in the left-hand menu.

3. Search for GitHub Copilot:

     In the Plugins tab, go to the Marketplace section.

     Type GitHub Copilot in the search bar.

4. Install the Plugin:

     Select the GitHub Copilot plugin from the search results and click Install.

5. Restart IntelliJ IDEA:

After installation, restart the IDE to activate the plugin

Step 3: Log In to GitHub

1. Activate GitHub Copilot:

Once IntelliJ restarts, you will be prompted to log in to GitHub.

Click the Sign in to GitHub button.

2. Authenticate via GitHub:

A browser window will open for authentication.

Log in to your GitHub account and authorize the plugin to access your GitHub account.

3. Complete Authentication:

After successful login, the browser will redirect you back to IntelliJ IDE.

Set up your account with all the configurations like billing details and other things which required to use copilot services. Once done below screen will appear.

Step 4: Configure GitHub Copilot Settings

Open Copilot Settings:

Go to File > Settings (or Preferences on macOS).

Navigate to GitHub Copilot in the left-hand menu.

Adjust Preferences:

Enable or disable specific features like inline suggestions or Copilot's behavior for different programming languages.

Set preferences for triggering suggestions (e.g., on typing).

Now set up is done. Once every thing is done, u can see the below image

Step 5: Start Using GitHub Copilot

Open a Project:

Open any project or file where you want to use Copilot.

Write Code:

Start typing, and GitHub Copilot will provide code suggestions automatically.

Accept Suggestions:

Use Tab or a similar keybinding (based on your settings) to accept Copilot’s suggestions.

Trigger Manual Suggestions:

Use the shortcut Ctrl + Space (Windows/Linux) or Command + Space (macOS) to manually request suggestions.

If you type your query in the above mentioned chat box, you will get response and include the same in your code.

This will enable developers to accelerate their coding process effectively.

How AI works simple in layman's terms

 

AI, or Artificial Intelligence, works by enabling computers to "learn" from data and make decisions or predictions without needing explicit programming for every possible situation. Here’s a simple breakdown of how AI works in layman's terms:

AI isn't "thinking" like humans do. It’s more like a sophisticated pattern-matching tool that learns from data and gets better at making decisions, predictions, or recommendations over time based on that data.

1. Learning from Data

Imagine AI as a child learning from examples. Instead of being told everything step-by-step, the child is shown lots of examples to recognize patterns. For example, to teach an AI to recognize pictures of cats, you show it thousands of images of cats, and over time, it starts to figure out what makes a cat a cat (fur, whiskers, eyes, etc.).

This process is called training. The more data the AI sees, the better it becomes at recognizing patterns.

2. Making Predictions

Once an AI is trained, it can be tested on new data it hasn’t seen before. If the AI was trained to recognize cats, you can show it a new picture, and based on what it has learned, it can predict whether the picture contains a cat or not.

This is like how you would recognize a cat even if you’ve never seen that particular one before. The AI doesn't "know" what a cat is the way humans do, but it uses patterns from the training data to guess.

3. Improving Over Time

Just like a human can get better with practice, AI can improve the more it learns. This process is often called machine learning, where the AI keeps adjusting itself to get more accurate predictions or decisions.

For example, a recommendation system like Netflix can learn your preferences over time by watching your behavior (what shows you watch, how long you watch them) and then get better at suggesting movies or shows you'll like.

4. Different Types of AI

There are different kinds of AI, but most can be grouped into:

Rule-based AI: Follows predefined rules to solve problems. Like a calculator – it knows the rules of math but doesn't "learn."

Machine learning AI: Learns from examples and improves over time. Most of the modern AI systems (like Siri, Alexa, and self-driving cars) use this.

Deep learning AI: A more advanced form of machine learning that uses networks similar to the human brain (called neural networks) to solve very complex problems like image recognition or understanding language.

5. Mimicking Human Tasks

AI can mimic a variety of human tasks, such as:

Recognizing objects: Like identifying faces in photos.

Understanding language: Chatbots or virtual assistants like Siri or Google Assistant.

Making decisions: AI used in self-driving cars or medical diagnostics.