Microsoft’s machine learning framework is now open source
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.
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.
“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.
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 5 October.
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.
"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.
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.
The sharing of Microsoft’s toys continued today with the open-sourcing of its model-based machine-learning framework, Infer.NET.
A team at Microsoft’s research centre in Cambridge, UK, kicked off development of the framework in 2004, and it was released for academic use in 2008. In Microsoft’s brave new world of AI, the technology has found itself evolving into a machine-learning engine and creeping into Office and Azure as well as gaming applications on Xbox.
Infer.NET, which is on GitHub right now, takes a model-based approach to machine learning. The developer gives the framework a model, and the framework then develops a machine-learning algorithm directly from the model provided.
You’d be forgiven for thinking it all sounds a bit back-to-front – many learning models require the programmer maps their model to a pre-existing learning algorithm, however, the Infer.NET engineers insist that their framework’s approach has the added advantage of interpretability. It should be possible for the user to work out why the system has behaved in a certain way having given it a model. As AI software become ever more prevalent, explaining its behavior becomes ever more important.
Test drive
Since the code is on GitHub (under the MIT license for free use in commercial applications) we obviously fired up Visual Studio and took the framework for a spin. Pulling the packages down via nuget proved problematic, however, simply cloning the source from GitHub and loading up the solution was enough to get the framework running. Eschewing the C# samples, we dived into the F# examples because hey, that’s the way we roll.
The thing is cross platform and 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 massaged into the code wrangler of your choice.
Models for Infer.NET are setup using a probabilistic program. The framework compiles this into what the team itself describes as something “cryptically called deterministic approximate Bayesian inference.” This is highly scalable, with Microsoft using it in a system that slurps knowledge from billions of web pages.
Sounding a little bit like the introduction to an old episode of the A-Team, the system can be summarized as: if you have a problem, and you can describe the model, and want to know why it just did that weird thing, or need it to learn when new data rolls in, then if you can download it, maybe you can use Infer.NET. ®
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.
“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.
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 5 October.
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.
"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.
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.
The sharing of Microsoft’s toys continued today with the open-sourcing of its model-based machine-learning framework, Infer.NET.
A team at Microsoft’s research centre in Cambridge, UK, kicked off development of the framework in 2004, and it was released for academic use in 2008. In Microsoft’s brave new world of AI, the technology has found itself evolving into a machine-learning engine and creeping into Office and Azure as well as gaming applications on Xbox.
Infer.NET, which is on GitHub right now, takes a model-based approach to machine learning. The developer gives the framework a model, and the framework then develops a machine-learning algorithm directly from the model provided.
You’d be forgiven for thinking it all sounds a bit back-to-front – many learning models require the programmer maps their model to a pre-existing learning algorithm, however, the Infer.NET engineers insist that their framework’s approach has the added advantage of interpretability. It should be possible for the user to work out why the system has behaved in a certain way having given it a model. As AI software become ever more prevalent, explaining its behavior becomes ever more important.
Test drive
Since the code is on GitHub (under the MIT license for free use in commercial applications) we obviously fired up Visual Studio and took the framework for a spin. Pulling the packages down via nuget proved problematic, however, simply cloning the source from GitHub and loading up the solution was enough to get the framework running. Eschewing the C# samples, we dived into the F# examples because hey, that’s the way we roll.
The thing is cross platform and 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 massaged into the code wrangler of your choice.
Models for Infer.NET are setup using a probabilistic program. The framework compiles this into what the team itself describes as something “cryptically called deterministic approximate Bayesian inference.” This is highly scalable, with Microsoft using it in a system that slurps knowledge from billions of web pages.Sounding a little bit like the introduction to an old episode of the A-Team, the system can be summarized as: if you have a problem, and you can describe the model, and want to know why it just did that weird thing, or need it to learn when new data rolls in, then if you can download it, maybe you can use Infer.NET. ®
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