Ray Summit 2025 - Scaling Batch Inference and RL

Watch Yi Sheng Ong and Eric Higgins, Software Engineers at Applied talk at Ray Summit 2025: Scaling Batch Inference and RL Applied Intuition uses Ray to scale large-scale inference and reinforcement learning workloads operating on petabytes of raw sensor data for autonomous driving. In this talk, we will cover Ray’s role within Applied’s ML infrastructure and how it enables unified, distributed execution across Kubernetes clusters. We discuss how Ray Data powers large-scale batch inference pipelines, streaming sensor data from our lake, performing CPU-intensive transformations, and seamlessly feeding into GPU inference at scale. Finally, we dive into how Ray’s distributed execution model and RLlib enable scalable open- and closed-loop reinforcement learning—running thousands of parallel rollouts, colocating GPU learners with simulators, and recovering full state efficiently during training. We also share our experience managing Ray infrastructure in production and practical tips for applying Ray to inference and reinforcement learning workloads.