Microsoft’s ML.NET: A blend of machine learning and .NET

The ultimate tech giant, Microsoft, recently announced a top-tier open source and cross-platform framework. The ML.NET is built to support model-based machine learning for .NET developers across the globe. It can also be used for academic purposes along with the research tool. And that isn’t even the best part. You can also integrate Infer.NET to be a part of ML.NET under the foundation for statistical modeling and online learning. This famous machine learning engine – used in Office, Xbox and Azure, is available on the GitHub for downloading its free version under the permissive MIT license in the commercial application.

But what is so unique about Infer.NET?

The Infer.NET helps to enable a model-based approach to the machine learning which lets you incorporate domain knowledge into the model. The framework is designed to build a speak-able machine learning algorithm directly from that model. That means, instead of having to map your problem onto a pre-existing learning algorithm, Infer.NET actually constructs a learning algorithm based on the model you have provided. This particular feature can come in handy if you know the behavior of the system. Or have extensive knowledge about the domain you are working in.

The Microsoft team is looking forward to engaging with the open source community in enhancing and developing a better framework. So, there’s no doubt why the team has integrated Infer.NET with the machine learning.

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Fundamental components of ML.NET

ML.NET basically comprised of two basic machine learning tasks: Classification i.e. text categorization or sentiment analysis and Regression i.ie. forecasting or price prediction. IT also supports anomaly detection, deep learning, recommendation systems, and natural language processing. It consists of various components in its architecture, as mentioned below.


To gain the advantage of various runtimes such as Tensor FLow, Accord.NET and Microsoft’s Cognitive Toolkit the new extensions are used by the ML.NET.

Data Transformation

Multiple data transforms like combiners and segregation, features, row filters etc. that enable the data transformation routines in the Pipeline API of machine learning is available in the latest version of ML.NET.


Components such as evaluators and collaborators are important for building machine learning capabilities like optimization and regularization.

Things to know before you start

Here are a couple things you need to keep in mind before you dabble in ML.NET.


We need to pick our best -fit machine learning algorithms before beginning with the machine learning. The algorithms consist of the clustering, regression, classification, and anomaly detection modules.

Model training

Train the machine learning model as it is the process of analyzing the input data by model. Training is mainly used to learn the pattern and save it for the model.

Let us assume an example where we create CSV file in our application in CSV file containing stock details such as ItemID, Location, InQTY, OutQTY, ItemType, and TotalStock quantity. We input 100 records in the file with all the necessary details. Give this file as an input to our model so it gets trained and analyzes the data using this data. This way once the model has trained the results are saved to understand the pattern.


After the model training is completed, the evaluation is performed. for evaluating the model, it is compared to the test data and produce the final predicted results.


It is also referred to as the prediction as of where score needs to have the same column as the trained model. This generates the results based on the trained model.

Major features

The framework brought the first draft of .NET APIs for training models and predictive models along with the few components such as learning algorithms, transforms and core machine-learning data structures. Next, it is going to deploy Pipeline application programming interface (API). ML.NET also intends to provide features for almost all languages such as C# and F# for the ease of users. It is also a viable and comprehensible effort for bridging the gap between .NET developers and communities working with machine learning models.

Model-based approach

The framework allows incorporating domain knowledge into their model to construct a learning algorithm for users based on a custom model.


If a user wants to design the model themselves and the learning algorithms that follow the model, the framework can understand why the system behaves in a particular way or makes predictions.

Other features

The framework supports all the system abilities to learn as the new data arrives. The Microsoft team is also working more towards developing and growing it further. Infer.NET is becoming a part of ML.NET which provides packages and namespaces to Microsoft.ML.Probabilistic and runs on Windows, Linux, Mac in command-line options which could be incorporated into the code developer of their choice.

Vision of ML.NET

AI framework growth

The ML.NET framework has opened all the doors leading to the open source community and provides a vast opportunity for all the .NET developers to work with the machine learning algorithms. It allows new namespaces to Microsoft.ML.Probabilistic which will extend the ML.NET for statistical modeling and online learning.

Internet of business

Infer.NET has support for online Bayesian inference that is the ability of the system to learn as new data arrives. It is a capable machine learning framework which is essential in business and consumer products that interact with users in real time. It allows the transparency which many machines learning methods lack at a time when people are becoming much more aware of the dangers of black box AI and data bias to call for neural networks on the rise.

There are exciting times ahead

We can conclude that ML.NET helps your business to improve the efficiency and reduce the cost as it is an open source as well as a cross-platform. The framework is proven, extensible, fast and reliable for your business to expand. The Microsoft team is still trying their level best to promote more progress and growth in this machine learning framework which is going to be crucial for the .NET developers. So, what are you waiting for? Begin with the ML.NET to avail all the special benefits if you desire to work in the machine learning framework.

Author avatar
Charles Richards

Charles Richards is a Business Analyst at Tatvasoft UK, a leading software outsourcing company in London. Besides his profession, Richards likes to share some new and trending technical aspects.

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