ML.NET– Machine Learning Framework for .NET Developers

Chudovo OU
  • Date Published
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Machine Learning and Artificial Learning are not the new kids on the block anymore

What Is a Machine Learning Framework?

Wondering what is a machine learning framework is? In simple terms, machine learning frameworks are specialized environments integrated with features and functions that help build machine learning models with speed and higher accuracy. With inbuilt machine learning algorithms, such a framework gives more power to a machine learning engineer helping build and deploy the required models. Microsoft ML.NET is one of many frameworks that allows a machine learning engineer, data scientist, and developer to complete the ML lifecycle.

Working with a machine learning framework, developers and business owners can expedite their efforts to integrate ML capabilities into their work. They can create the right and adaptable machine learning aspects into their digital solutions and leverage the benefits that come along with including machine learning into everyday business. Furthermore, companies using a machine learning framework like ML.NET and others can enhance the speed of their project. This means that in a competitive industry, they can substantially reduce their time to market and still provide their users with an advanced application. Moving forward with our discussion on what is Microsoft ML.NET, we will talk about the different characteristics of the framework, its features, and its advantages.

Model Builder

The Microsoft AI ML.NET framework brings the ability to work with machine learning into Visual Studio. A small ML.NET tutorial will help you learn how to integrate the modules and API models of machine learning and render them via Visual Studio. Microsoft provides a convenient and faster visual interface with the model builder to build, train, and deploy tailor-made machine learning models into the application. In other words, working with ML.NET model builder does not require anyone to gain expertise in machine learning.

Furthermore, the model builder also has an automatic setting to explore and find the right machine learning settings for your application. With the help of AutoML, the ML.NET machine learning framework model builder will automatically help you integrate the perfect settings and modules required for your product.

Model Explainability

The concept of decision-making in Machine Learning and Artificial Intelligence has long been a contentious issue debated globally. With machine learning models acting as opaque boxes where we know what’s going on and what comes out, it is quite intriguing to implement these models in our daily lives without knowing the intermediate steps. A simple example of this is a machine or a robot making healthcare decisions based on the data fed to the same. But the human doctors will need to understand how the robot reached the conclusion and what factors were taken into consideration for the same. Model explainability in this machine learning framework is addressed with Permutation Feature Importance (PFI). With PFI, ML.NET delivers an ImmutableArray of RegressionMetricsStatistics.

The PFI technique is purposeful in explaining the classification and regression models taken from Breiman’s Random Forests paper. With the help of these regression models, ML.NET shuffles the entire dataset and calculates the intensity of change in the performance metric. A larger change in the performance metric means that a particular feature is more important and vice versa. Taking out health diagnosis again, a machine learning framework will take into account all the factors that could lead to the present condition. By effectuating an increase and decrease in the factors, the ML.NET-powered solution will then discover the factors that had the highest role to bring about that change.