Using Jupyter/IPython Notebooks in Azure ML

Download this episode

Download Video


Jupyter notebooks (formerly IPython) provide a highly productive canvas for data scientists and developers to explore ideas.  At its heart, Jupyter is a multi-lingual REPL (read eval print loop), where you can enter some code, and get a response.  The response can be program output, a graph, etc.  The notebook is comprised of interspersed code and markdown text for documentation purposes.  For examples of some Notebooks, take a look at

We're delighted to announce the availability of Jupyter notebooks as a service on Azure ML.  It is integrated with the Azure ML Studio, which means you can explore your datasets, write code, build models, etc. conveniently from a Notebook.   Want to use Pandas or Seaborn to check out a data set, visualize it, slice/dice it and store it back?  Simple: just click the data set in the Studio and select "Open in Notebook".  Best of all there is no installation required.  You can use Jupyter notebooks with AML from any modern browser from any OS.



Available formats for this video:

Actual format may change based on video formats available and browser capability.

    The Discussion

    Comments closed

    Comments have been closed since this content was published more than 30 days ago, but if you'd like to send us feedback you can Contact Us.