https://106c4.wpc.azureedge.net/80106C4/Gallery-Prod/cdn/2015-02-24/prod20161101-microsoft-windowsazure-gallery/Microsoft.MachineLearningExperimentation.1.1.1-preview/Icons/Large.png

Machine Learning Experimentation

Microsoft
Prepare your data, author models, and track your training history.
https://106c4.wpc.azureedge.net/80106C4/Gallery-Prod/cdn/2015-02-24/prod20161101-microsoft-windowsazure-gallery/Microsoft.MachineLearningExperimentation.1.1.1-preview/Screenshots/Image01.png
https://106c4.wpc.azureedge.net/80106C4/Gallery-Prod/cdn/2015-02-24/prod20161101-microsoft-windowsazure-gallery/Microsoft.MachineLearningExperimentation.1.1.1-preview/Screenshots/Image01.png

Machine Learning Experimentation

Microsoft
Prepare your data, author models, and track your training history.

The preview Experimentation capabilities of Azure Machine Learning provide access to tools, on a per-seat basis, that allow you to prepare data, engineer features, and author models in the compute environment of your choice with built-in analytics such as metrics and run history.

Leverage the Experimentation capabilities along with Azure Machine Learning Model Management capabilities to complete your data science workflow from data preparation and model creation through to model deployment and management in production.

The preview Experimentation capabilities include:

  • Open Platform: Create your data science solutions using open source libraries and familiar open source tools such as Visual Studio Code, RStudio, Git, and others.
  • Control and Flexibility with Bring Your Own Compute: Easily execute jobs and scale up or out with a select set of supported computes to suit your execution needs. Includes support for GPUs for deep learning.
  • Intelligent Data Preparation: Use an intuitive and powerful machine learning based data preparation experience built on the PROSE (Program Synthesis using Example) technology.
  • Agile Experimentation: Perform experiment execution in a variety of compute contexts and run multiple experiments simultaneously.
  • Data Science Provenance: Ensure auditability and reproducibility of your experiments through a comprehensive run history and version control system.
  • Collaboration and Sharing: Facilitate collaboration by your team through built-in Git repositories.
https://106c4.wpc.azureedge.net/80106C4/Gallery-Prod/cdn/2015-02-24/prod20161101-microsoft-windowsazure-gallery/Microsoft.MachineLearningExperimentation.1.1.1-preview/Screenshots/Image01.png
https://106c4.wpc.azureedge.net/80106C4/Gallery-Prod/cdn/2015-02-24/prod20161101-microsoft-windowsazure-gallery/Microsoft.MachineLearningExperimentation.1.1.1-preview/Screenshots/Image01.png