As part of our ongoing commitment to open and interoperable artificial intelligence, Microsoft has joined the SciKit-learn consortium as a platinum member and released tools to enable increased usage of SciKit-learn pipelines.

Initially launched in 2007 by members of the Python scientific community, SciKit-learn has attracted a large community of active developers who have turned it into a first class, open source library used by many companies and individuals around the world for scenarios ranging from fraud detection to process optimization. Following SciKit-learn’s remarkable success, the SciKit-learn consortium was launched in September 2018 by Inria, the French national institute for research in computer science, to foster growth and sustainability of the library, employing central contributors to maintain high standards and develop new features. We are extremely supportive of what the SciKit-learn community has accomplished so far and want to see it continue to thrive and expand. By joining the newly formed SciKit-learn consortium, we will support central contributors to ensure that SciKit-learn remains a high-quality project while also tackling new features in conjunction with the fabulous community of users and developers.

In addition to supporting SciKit-learn development, we are committed to helping Scikit-learn users in training and production scenarios through our own services and open source projects. We released support for using SciKit-learn in inference scenarios through the high performance, cross platform ONNX Runtime. The SKlearn-ONNX converter exports common SciKit-learn pipelines directly to the ONNX-ML standard format. In doing so, these models can now be used on Linux, Windows, or Mac with ONNX Runtime for improved performance and portability.

We also provide strong support for SciKit-learn training in Azure Machine Learning. Using the service, you can take existing Scikit-learn training scripts and scale up your training to the cloud, automatically iterate through hyperparameters to tune your model, and log experiments with minimal effort. Furthermore, the automated machine learning capability can automatically generate the best SciKit-learn pipeline according to your training data and problem scenario.

At Microsoft we believe bringing AI advances to all developers, on any platform, using any language, in an open and interoperable AI ecosystem, will help ensure AI is more accessible and valuable to all. We are excited to be part of the SciKit-learn consortium and supporting a fantastic community of Scikit-learn developers and users.

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