ML.NET Model Builder - An Overview

In this article, we are going to learning about ML.NET Model Builder and to use it

What is ML.NET Model Builder?

 
As we know, Machine Learning is among the most powerful tools of this era and Microsoft has introduced the Model Builder tool for Machine Learning. Yes, we are talking about ML.NET Model Builder.
 
ML.NET is an open-source, cross-platform machine learning tool for .Net Core and .Net, and we can deploy it on Windows, Mac OS, and Linux. Model Builder provides us with a simple UI to build, train and ship machine learning model. You can download ML.NET Model Builder extension for Visual Studio.
 

What are the Steps of Model Builder?

 
ML.NET Model Builder is a tool for the developer who has no expertise on Machine Learning because, here, we can get the data from files which have already stored into our disk or connects to the Relational Database like MS-SQL Server to train your model and the developers can generate their code.
 
ML.NET Model Builder - An Overview
 
Model Builder provides us with the following steps to generate the code for our application.
  1. Scenario
  2. Data
  3. Train
  4. Evaluate
  5. Code

Scenario

 
In here, you need to choose a scenario. ML.NET Model Builder has provided a couple of templates like Sentiment Analysis, Issue Classification, Price Prediction, and Custom Scenario.
 
ML.NET Model Builder - An Overview
 

Data


To run a model, you need some data and the data source can be CSV, TSV files, SQL Server, or you can also download it from Wikipedia detox dataset. Select the file or connect to the SQL Server and define the Lable or Column for prediction. Column/ Label is an output parameter that actually you want to predict.
 
ML.NET Model Builder - An Overview
 

Train

 
To get a proper result, you need to train your model properly and AutoML finds the best performing model for you. Now, the question is, what is AutoML?  AutoML is an automated process for building a model that Microsoft has included in the ML.NET framework. But if you select Custom Scenario, then you need to define the task. ML.NET will give us a few Machine Learning tasks like multiclass-classification, binary-classification, and regression.
 
ML.NET Model Builder - An Overview
 

Evaluate

 
In the phase of evaluation, we generalize the accuracy of a model by training and testing. The framework provides us the best model accuracy and the best model. The most important thing here is that when we choose a model, each scenario maps with a task and each task has its own set of metrics for evaluation. The following table describes the mapping of the scenario and the task details.
 
ML.NET Model Builder - An Overview
 

Code

 
The last step is to generate the code. Here, you can generate the code and add the code into your project.
 
ML.NET Model Builder - An Overview
 
Microsoft has really brought an easy and understandable solution for Machine Learning lovers with a great visual interface to build, train, and deploy their own custom machine learning data models.