Important update for Azure Machine Learning

9 Aralık 2014 Salı

The December 8, 2014 release of Azure Machine Learning includes a major version upgrade of the modules and machine learning framework. For more information, please see Major Version Upgrade on 12/8/14 May Require User Action. New features and improvements
  • New modules that are available:
    • Two-Class Locally-Deep Support Vector Machine module
    • Pre-trained Cascade Image Classifier module for face detection, using OpenCV library.
    • Image Reader module that uses OpenCV library to read images from Azure Blob storage
  • Sweep Parameters module supports cross-validation mode, in addition to the train-test model. The test input port becomes optional. If it is left unconnected, cross-validation mode is used.
  • Sweep Parameters module orders the output table by the selected evaluation metric.
  • Decision Forest Regression returns standard deviation for scored labels.
  • Timeouts for Reader and Writer modules is increased. The issue with 15-minute default timeout for Hive ingress has been addressed by increasing the timeout to 24 hours.
  • In the Filter-Based Feature Selection module, Kendall, Pearson, and Spearman correlations use conditional mean expectation value in cases when one column is numeric and other one is categorical.
  • Evaluate Recommender module can automatically infer the type of recommendation based on column names.
In addition:
  • Removed the Ready to Deploy to Production button from Studio. This enables publishing the Web Service from an experiment as a "default end point" without the need to take any additional action to deploy it to production.
    • The published "default end point" is shown in the Azure portal under Web Services. There, a new end point can be created from the default end point, if needed (for example, to share with a customer). In the Studio, the additional end point count will show as "0" until a new end point is created in the Azure portal. (Currently, this feature is limited to adding one end point in addition to the default.)
  • When you create a new experiment, the experiment tile names in the gallery of sample experiments wrap so that you see the entire experiment name.
  • When you create or edit an experiment, you'll notice that the resize area between the experiment canvas and the module palette is increased.
For more information or questions about this release, please visit the Microsoft Azure Machine Learning Forum.

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