Apache SystemML is a system and language that supports rapid development of custom machine learning algorithms for large scale problems. SystemML allows data scientists to write code once in terms of high-level linear algebra operations, then automatically generate low-level parallel versions of the program that are tuned to the characteristics of the data and different parallel execution frameworks. The system consists of two major components: An optimizer that automatically parallelizes high-level code; and a runtime that evaluates the resulting execution plans at scale on Apache Hadoop, on Apache Spark, on large multi-core systems, and, more recently, on GPUs. This talk will start by describing the history of the project. I'll explain how the original research team from IBM advanced the state of the art in automatic parallelization and scalable linear algebra to build the optimizer and runtime, and how we turned the resulting research code into Apache SystemML. I'll describe how Apache SystemML has been used to implement state-of-the-art algorithms in the field. Finally, I'll talk about recent work on enhancing the system with compressed linear algebra, automatic generation of custom linear algebra kernels, and support for deep learning.
