Matthias Boehm: Declarative Machine Learning for Low-Latency to Large-Scale Deployments
Seminar | October 5 | 12:30-1:30 p.m. | Soda Hall, Wozniak Lounge
Matthias Boehm, IBM
Declarative machine learning (ML) aims to simplify the development and usage of large-scale ML algorithms. In SystemML, data scientists specify ML algorithms in a high-level language with R-like syntax and the system automatically generates hybrid execution plans that combine single-node, in-memory operations and distributed operations on Spark. In a first part, we motivate declarative ML and provide an up-to-date overview of SystemML including its APIs for different deployments. Since it was rarely mentioned before, we specifically discuss a programmatic API for low-latency scoring and its usage in containerized and data-parallel environments. In a second part, we then discuss selected research results for large-scale ML, specifically, compressed linear algebra (CLA) and automatic operator fusion. CLA aims to fit larger datasets into available memory by applying lightweight database compression schemes to matrices and executing linear algebra operations directly on the compressed representations. In contrast, automatic operator fusion aims at avoiding materialized intermediates and unnecessary scans, as well as sparsity exploitation by optimizing fusion plans and generating code for these custom fused operators. Together, CLA and automatic operator fusion achieve significant end-to-end improvements as they address orthogonal bottlenecks of large-scale ML algorithms.