Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon

Microsoft open sources Infer.NET, it’s popular model-based machine learning framework

Save for later
  • 3 min read
  • 08 Oct 2018

article-image

Last week, Microsoft open sourced Infer.NET, the cross-platform framework used for model-based machine learning. This popular machine learning engine used in Office, Xbox and Azure, will be available on GitHub under the permissive MIT license for free use in commercial applications.

Features of  Infer.NET


The team at Microsoft Research in Cambridge initially envisioned Infer.NET as a research tool and released it for academic use in 2008. The framework has served as a base to publish hundreds of papers across a variety of fields, including information retrieval and healthcare. The team then started using the framework as a machine learning engine within a wide range of Microsoft products.

A model-based approach to machine learning


Infer.NET allows users to incorporate domain knowledge into their model. The framework can be used to build bespoke machine learning algorithms directly from their model. To sum it up, this framework actually constructs a learning algorithm for users based on the model they have provided.

Facilitates interpretability


Infer.NET also facilitates interpretability. If users have designed the model themselves and the learning algorithm follows that model, they can understand why the system behaves in a particular way or makes certain predictions.

Unlock access to the largest independent learning library in Tech for FREE!
Get unlimited access to 7500+ expert-authored eBooks and video courses covering every tech area you can think of.
Renews at €18.99/month. Cancel anytime

Probabilistic Approach


In Infer.NET, models are described using a probabilistic program. This is used to describe real-world processes in a language that machines understand. Infer.NET compiles the probabilistic program into high-performance code for implementing something cryptically called deterministic approximate Bayesian inference. This approach allows a notable amount of scalability. For instance, it can be used in a system that automatically extracts knowledge from billions of web pages, comprising petabytes of data.

Additional Features


The framework also supports the ability of the system to learn as new data arrives. The team is also working towards developing and growing it further. Infer.NET will become a part of ML.NET (the machine learning framework for .NET developers). They have already set up the repository under the .NET Foundation and moved the package and namespaces to Microsoft.ML.Probabilistic.  Being cross platform, Infer.NET supports .NET Framework 4.6.1, .NET Core 2.0, and Mono 5.0. Windows users get to use Visual Studio 2017, while macOS and Linux folks have command-line options, which could be incorporated into the code wrangler of their choice.

Download the framework to learn more about Infer.NET. You can also check the documentation for a detailed User Guide. To know more about this news, head over to Microsoft’s official blog.

Microsoft announces new Surface devices to enhance user productivity, with style and elegance

Neural Network Intelligence: Microsoft’s open source automated machine learning toolkit

Microsoft’s new neural text-to-speech service lets machines speak like people