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NumFOCUS Platinum Sponsor IBM has been doing wonderful work to support one of our fiscally sponsored projects, Project Jupyter. Brian Granger over at the Jupyter Blog has the details…

“For the past few years, Project Jupyter has been collaborating with IBM on a number of initiatives. Much of this work has happened in the Jupyter Incubation Program, where IBM has been working on extensions for Jupyter that add dashboardingimproved notebook content management, a kernel gateway, and declarative interactive widgets.
IBM has also released the Data Science Experience (DSX), a Jupyter Notebook based data science cloud platform. “Jupyter notebooks are the primary user interface for our portfolio of machine learning offerings,” says Jean-Francois Puget, of IBM Analytics.

In parallel to that work, IBM has been investing significant resources to bring the power of Jupyter and its many kernels to traditional mainframe systems based on z/OS. Early in 2016, IBM and Rocket Software released the z/OS Platform for Apache Spark. This platform enables optimized abstraction and real-time analysis of structured and unstructured enterprise data using the full power of Apache Spark. This release also included a complement of open sources tools (http://zos-spark.github.io/), including a Spark/Scala Kernel for Jupyter called Apache Torre.

Last month, IBM made several announcements around Project Jupyter as part of a broader open data science platform strategy on z/OS.

First, Continuum Analytics has joined the z/OS partner ecosystem to collaborate with IBM and Rocket Software to bring Anaconda to z/OS. This will include a full blown Jupyter experience on z/OS for Python and R. With this release, the Jupyter Notebook provides z/OS users with a unified way to access the many powerful analytics and machine learning tools from Apache Spark and Anaconda, including scikit-learn, Pandas and dask.

Second, IBM announced IBM Machine Learning, which combines its Data Science Experience interface with Spark ML for z/OS to provide an end to end machine learning experience for z/OS…”

To continue reading, head on over to the Jupyter Blog.

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