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 Data Scientist and Developer Advocate at the IBM Center for Open Source Data and Artificial Intelligence Technology (CODAIT). His duties include enablement and advocacy for IBM clients and the community at large, which includes teaching, community outreach, consulting and technical support for open source AI projects which includes Apache Spark, Tensorflow and others.Jerome has been with IBM as a data scientist since 2015. 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.