Building Custom Machine Learning Algorithms With Apache SystemML

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Apache SystemML is an open-source language and compiler that makes it dramatically easier to build custom machine learning solutions that scale automatically to massive data sizes. This talk will show how to deploy and use SystemML for algorithm development. I will start with some instructive examples of the importance of algorithm customization in machine learning. I’ll show how algorithms that appear very similar can produce dramatically different results. Then I’ll walk through the process of building a custom algorithm using Apache SystemML, starting with the software download and ending with running the new algorithm in parallel on Spark.

About Frederick Reiss

Fred Reiss is Chief Architect at the IBM Spark Technology Center in San Francisco and is one of the founding employees of the Center. Fred received his Ph.D. from UC Berkeley in 2006, then worked for IBM Research Almaden for the next nine years. At Almaden, Fred worked on the SystemML and SystemT projects, as well as on the research prototype of DB2 with BLU Acceleration. Fred has over 25 peer-reviewed publications and six patents.