Modern Compute Stack for Scaling Large AI/ML/LLM Workloads at QCon San Francisco

Jules Damji, a lead developer advocate at Anyscale Inc., discussed the difficulties data scientists encounter when managing infrastructure for machine learning models. He emphasized the necessity for a framework that supports the latest machine learning libraries, is easily manageable, and can scale to accommodate large datasets and models. Damji introduced Ray as a potential solution.

ai anyscale artificial intelligence compute data data pipelines data scientists datasets developer framework infrastructure large datasets libraries llm machine machine learning machine learning models ml & data engineering modern python qcon qcon san francisco 2023 san francisco scale scaling scientists stack workloads