The Tip of the Iceberg: How to make ML for Systems work
Machine Learning has become a powerful tool to improve computer systems and there is a significant amount of research ongoing both in academia and industry to tackle systems problems using Machine Learning. Most work focuses on learning patterns and replacing heuristics with these learned patterns to solve systems problems such as compiler optimization, query optimization, failure detection, indexing, caching. However, solutions that truly improve systems need to maintain the efficiency, availability, reliability, and maintainability of systems while integrating Machine Learning into the system. In this talk, I will cover the key aspects and surprising joys of designing, implementing and deploying ML for Systems solutions based on my experiences of building and deploying these systems at Google.
Deniz is a Software Engineer in the ML for Systems team at Google Brain Research. She received her PhD in distributed systems from Cornell University in 2017 under the supervision of Robbert van Renesse, specializing on consensus protocols and self-adapting systems. Since her PhD she has worked on large-scale database systems and learned systems. Currently, she is focusing on improving the state-of-the-art in systems using Machine Learning, mainly focusing on caching and indexing. Her work has appeared in top-tier conferences and workshops such as SOSP and ML for Systems at NeurIPS.
Time and Place
October 25, 2022 @ 1:30PM in MH 225