Systems and ML at RISELab

In this talk, I will present several of the projects we are developing at RISELab, a three-year old lab at UC Berkeley that focuses on building platforms and algorithms for real-time intelligent decisions, decisions that are secure and explainable. These projects include both systems to better support machine learning (ML) workloads, and leveraging ML to build better systems. In the first category, I will present Ray, a general-purpose distributed system which provides both task-parallel and actor abstractions. Ray already supports several popular libraries, including a reinforcement learning library (RLlib) and a hyperparameter search library (Tune), and it is deployed in production at tens of organizations. In the second category, I will present Autopandas, a system that synthesizes snippets of API calls from input-output examples for Pandas, the most popular data science library today, and NeuroCuts, a tool to generate decision trees that implement network packet classifiers.