Apache systemML is IBM's open source project that interfaces with the Spark Context, allowing for simple expression of numerical algorithms. This is an ideal platform for Data Science, especially when there is an interest in specializing machine learning algorithms for specific challenges. The platform is extremely flexible, and enables complex numerical algorithms to be expressed in a simple and readable syntax, while preserving scalability for heavy duty computations. The parallelization details are optimized through the powerful cost based optimization engine in systemML.
Jerome Nilmeier is a developer advocate, data scientist, and member of the IBM Center for Open source Data and AI Technologies (CODAIT), where he works with with open source frameworks for big data, machine learning, and deep learning as a developer advocate. He has recently published an O'Reilly Manual, "Data Science and Engineering at Enterprise Scale", which is a great introductory text for data scientists interested in machine learning, big data, and AI.
He has a BS in Chemical Engineering from UC Berkeley, a PhD in Computational Biophysics from UC San Francisco, and has carried out postdoctoral research in biophysics and bioinformatics at UC Berkeley, Lawrence Berkeley and Livermore Laboratories, and at Stanford as an OpenMM Fellow. Just prior to joining IBM, he completed the Insight Data Engineering program in late 2014. He has been with IBM since 2015.