Machine Learning

Welcome! The Azure Machine Learning team invites you to share and vote for features to help you build, manage, and deploy custom machine learning models.

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  1. Expose API for model building / publishing automation

    When offering ML Prediction as Service to our customers it can be crucial to automate model creation, as different models need to be generated for different customers. It'd probably be enough to enable importing R scripts via an API or WebService call to solve this.

    164 votes
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    7 comments  ·  Flag idea as inappropriate…  ·  Admin →
  2. Enable re-training of the model via WebService / API call

    Many live-data scenarios such as algorithmic trading require frequent re-training of the model when new live data comes in. In this case the model needs to be re-trained based on the new data window. For these kinds of applications to be feasible with Azure ML, it's required that the re-training / publishing of the model can be automated. It's not necessary that this is solved using a WebService call, it could be done using some exposed API call from .NET, etc.

    174 votes
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    10 comments  ·  Flag idea as inappropriate…  ·  Admin →
  3. Ask for more unsupervised classification Models

    Any unsupervised classification libraries you have exposed or plan to expose? I only have unlabeled raw data and do not want to label them one by one.

    8 votes
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    0 comments  ·  Flag idea as inappropriate…  ·  Admin →
  4. Recommendation API tutorial

    Please provide a tutorial on how to recommend movies etc to someone using Azure ML based off of previous customer history (cold start) and your own history.

    Similar to http://blogs.technet.com/b/machinelearning/archive/2014/07/09/recommendations-everywhere.aspx

    40 votes
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    1 comment  ·  Flag idea as inappropriate…  ·  Admin →
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