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Microsoft Opens its Machine Learning Framework For All

Infer.NET was used to publish hundreds of research papers using a variety of fields, everything from information retrieval to healthcare

Global tech giant Microsoft has opened its cross-platform frameworks Infer.NET — a machine learning engine used in Office, Xbox and Azure — for one and all worldwide.

“We’re extremely excited today to open source Infer.NET on GitHub under the permissive MIT license for free use in commercial applications,” Yordan Zaykov, Principal Research Software Engineering Lead at Microsoft, wrote in a blog post on Friday.

Developed in 2014 by Microsoft’s research lab in Cambridge, Infer.NET was initially envisioned as a research tool and later in 2008 was released for academic use.

Infer.NET enables a model-based approach to machine learning. It lets users incorporate domain knowledge into their model.

The framework can then build a bespoke machine learning algorithm directly from that model.

Microsoft
A sign for Microsoft is seen on a building in Cambridge. VOA

“This means that instead of having to map your problem onto a pre-existing learning algorithm that you’ve been given, Infer.NET actually constructs a learning algorithm for you, based on the model you’ve provided,” Zaykov said.

He noted that the Infer.NET team is looking forward to engaging with the open-source community in developing. Infer.NET will become a part of ML.NET — the machine learning framework for .NET developers.

“We have already taken several steps towards integration with ML.NET, like setting up the repository under the .NET Foundation and moving the package and namespaces to Microsoft.ML.Probabilistic. Infer.NET will extend ML.NET for statistical modelling and online learning,” Zaykov said.

Also Read- India-Russia Sign An Air Defense System Deal

Infer.NET was used to publish hundreds of research papers using a variety of fields, everything from information retrieval to healthcare.

In 2012 Infer.NET even won a Patents for Humanity award for aiding research in epidemiology, genetic causes of disease, deforestation and asthma. (IANS)

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