Home Tech news The Microsoft Infer.NET machine learning framework goes open source

The Microsoft Infer.NET machine learning framework goes open source

Udit Agarwal
Startup/Tech News Correspondent, Responsible for gathering information on the tech companies working on IOT, AI, ML, Cloud, Mobile Technologies, Udit can be reached at [email protected]

Microsoft Corp. today open-sourced Infer.NET, an internally developed machine learning engine that it uses to power parts of Azure, Office 365 and the Xbox video game platform.

The company has made the code for the tool available on GitHub under the permissive MIT license, which allows free commercial use. The move to open up Infer.NET comes nearly 15 years after the first iteration of the software was developed at the company’s Cambridge, U.K. research lab.

Yordan Zaykov, an engineering lead with the team (pictured) behind Infer.NET, detailed the engine’s evolution in a blog post. He wrote that the software started its life as a research tool and has been used in the creation of hundreds of academic papers across fields ranging from epidemiology to forest conservation. Over the years, Infer.NET evolved into a scalable engine that Zaykov wrote and now helps process petabytes of data across different Microsoft services.

The engine differs from many of the other open-source machine learning tools out there. Infer.NET is designed to facilitate a “model-based” approach to building artificial intelligence software, which reverses the normal development workflow.

When working with a conventional machine learning tool, engineers typically find an existing AI algorithm and retrofit it to their project’s requirements. Infer.NET, in contrast, uses these requirements as the starting point. The tool enables engineers to express project-specific information as a model and uses the model to generate a new custom AI algorithm optimized for the task at hand.

This approach makes Infer.NET well-suited for projects that rely on large amounts of domain-specific knowledge. Moreover, the fact that the behavior of AI algorithms created with the tool is directly shaped by the model on which they’re based provides much-needed visibility into their inner workings.

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