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Jupyter Notebooks and ML Model Operationalization


Sponsor Talk- Microsoft Jupyter (formerly IPython) notebooks provide a convenient canvas for exploring and visualizing data. Jupyter has quickly become the preferred “IDE” for many Data Science and Technical Computing scenarios. Azure ML Studio is an easy to use, drag/drop IDE that provides the ability to build and deploy web services that expose Machine Learning models as RESTful APIs (a.k.a. Operationalization). We’re happy to announce the we’ve incorporated Jupyter with the Azure ML Studio.

Beyond the integration, with the release of the AzureML APIs in the Python SDK, we are bringing the ease of publishing high scale predictive web services that run on Azure to Jupyter and other IDE’s. Using this SDK, you can now do the data manipulation and feature engineering using the REPL experience that Jupyter provides and then publish the final model as an Azure ML web service. The published web services provide a RESTful interface that can then be called from variety of platforms and clients such as Excel, .NET, Java, Python, R, etc.


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