ML.Net – A Machine Learning Framework for .Net Developers

07 Sep 2022
5 min read  

If we want to prepare a list of current most hot and discussed technology, then one of the technologies in that list must be a Machine Learning Technique. In today's IT industry or software development world, machine learning is one of the tops most technology. But when we talked about Machine Learning, then we confused about the features or behavior of this technology. Is it the same as “BigData” or maybe like “data science”? Is it different from the data statistics? In general, machine learning is the process or mechanism which totally depends on a huge amount of mathematics and algorithms. Actually, Machine Learning is providing a transition process from the art or science methodology into a technology process for every developer or programmer. In fact, in near future, every application which may be developed in any technology must be incorporated with the trained models so that application can perform any type of data-driven decisions on the basis of those trained model. This process is always a tremendous engineering challenge because in now-a-days, data science and modeling are basically decoupling from each other in a software development process.

So, the question is What is Machine Learning? In a simple word, machine learnings are basically writing a program which learns how to perform a particular task as per the experience, without being explicitly programmed that perform operations. So, the main concept in machine learning is to write a program that will perform some task automatically. The program can be learning the behavior from the experience, may be on the basis of existing data samples of the past observations or by analyses the data programmatically for doing the job.

What is ML.Net?

ML.Net basically is an Open-Source and Cross-Platform machine learning framework provided by Microsoft for the.Net developers. Since ML.Net is a Cross-Platform framework, so it can be used in both Windows, Linux, and Mac OS. With the help of ML.Net, we can create or develop custom ML models using C# or F# languages without leaving the.Net framework platform. Also, with this framework, developers can incorporate the AI (Artificial Inelegancy) to any applications by developing custom machine learning trained models for any type of scenario like Sentiment Analysis, Sales Forecasts, Future Price Predictions, Customer Satisfaction and many more.

ML.NeT Version History

ML.Net is introduced by the Microsoft for the first time in May 2018 for the public release. But Microsoft internally used the ML.Net technology since last few years in some own products like – Bing Ads, Office, Windows, Azure etc. after the first public release of the ML.Net in May 2018, Microsoft released a new version in every month from then and finally in May 2019, Microsoft released the ML.Net 1.0.

ML.Net Features

Microsoft released the ML.Net as the Open Source framework after the research of nearly 10 years to provide an opportunity for the developer to build a modern application with customized machine learnings. ML.Net 1.0 release includes the below features :

  • ML.Net is an open-source and cross-platform based framework.

  • The first version of ML.Net provides trained and predictive models.

  • Also, ML.Net supports some core components like transforms, core-machine learning-based data structure and also some sample learning algorithm.

  • ML.Net provide improvement in the efficiency of the applications.

  • ML.Net is much faster and more reliable.

ML.Net Advantages

So, now we have a fair idea about what is machine learning? ML.Net or Machine Learning always promises to solve the problem in a compressive manner and provide many benefits to the user by making the correct predictions and help them to take the correct decisions. Below are the main advantages of the ML.Net

  • ML.Net can check a large volume of data set and then provide the possible trends and patterns of result according to that data.

  • It is total automation process for data analysis. No human interactions required during the execution of the ML.Net projects

  • With the increase of experience in term of data sample volume, the algorithm written in the ML.Net will become much more accurate and efficient.

  • .ML.Net algorithms are very much efficient to handle data which are multi-dimensional and multi-variety.

  • The implication of ML.Net based applications is very wide. We can use ML.Net applications in any industry like Health Care, Marketing, Sales, etc. for analysis of the customer requirements or choice.

Getting Started With ML.Net

We can develop an application using Machine Learning in either C# of F# languages. Microsoft provides the support of the ML.Net in Microsoft Visual Studio. For this, we need to install Microsoft Visual Studio 2017 15.6 or later version. Also, with this product installation, we also need to download and install the ML.Net Model builder tool from the Microsoft web sites. Model Builder is simply a GUI tool for developers where we can build, train and prepare the custom machine learning models for any type of application. We can connect our data which are stored either in a database or in model files and then train the model and then generate the code for the purpose of the model training and consumption. This model builder tool can be downloaded from URL -


So, now a days, Machine Learning is one of the most popular techniques for any application developer. Since we assume that this will solve our many problems and predicts the product expected result as per the trained data model. But, still, Microsoft evolves the ML.Net and expected to release more features in the ML.Net soon. In this article, we discuss the basic concept of ML.Net, advantages of ML.Net including their features and also the version history.

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About Author
Debasis Saha (Technical Manager, Author and Mentor)

He is an ASP.Net, MVC and Angular Technical Manager with 10 years of experience. He always been a great fan of Microsoft Technologies and loves working on them. He has expertise in Asp.Net, MVC, SQL Server, MongoDB, Angular JS, and Angular. He loves to write articles about these technologies.
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