ML.NET is a cross-platform, machine learning framework for .NET developers, and Model Builder is the UI tooling in Visual Studio that uses Automated Machine Learning (AutoML) to easily allow you to train and consume custom ML.NET models.
We are excited to announce the release of ML.NET 1.0 today. ML.NET is a free, cross-platform and open source machine learning framework designed to bring the power of machine learning (ML) into .NET applications.
Get Started @ http://dot.net/ml
ML.NET allows you to train,
Today at Spark + AI summit we are excited to announce .NET for Apache Spark. Spark is a popular open source distributed processing engine for analytics over large data sets. Spark can be used for processing batches of data, real-time streams,
Announcing ML.NET 0.9 – Machine Learning for .NET
ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models.
We’re excited to announce today the release of ML.NET 0.7 – the latest release of the cross-platform and open source machine learning framework for .NET developers (ML.NET 0.1 was released at //Build 2018). This release focuses on enabling better support for recommendation based ML tasks,
Today we’re announcing our latest monthly release: ML.NET 0.6! ML.NET is a cross-platform, open source machine learning framework for .NET developers. We want to enable every .NET developer to train and use machine learning models in their applications and services. If you haven’t tried ML.NET yet,
Today, coinciding with the .NET Conf 2018, we’re announcing the release of ML.NET 0.5. It’s been a few months already since we released ML.NET 0.1 at //Build 2018, a cross-platform, open source machine learning framework for .NET developers. While we’re evolving through new preview releases,
Tensor is an exchange type for homogenous multi-dimensional data for 1 to N dimensions. The motivation behind introducing Tensor<T> is to make it easy for Machine Learning library vendors like CNTK, Tensorflow, Caffe, Scikit-Learn to port their libraries over to .NET with minimal dependencies in place.
⏱ Updated on June 28, 2017
🙌 Welcome to the first of .NET’s new AI and Machine Learning themed blog entries! We have set up this space as a place to share and discuss the work we will be doing with AI and Machine Learning.
Why you should use F#
This post was written by Phillip Carter and Mads Torgersen. Both work on languages on the .NET team.
At Build 2017, we presented a tech talk entitled “Why You Should Use F#”. However, not everyone can attend Build,