Contains hands-on materials covering Ray’s ecosystem for scaling deep learning, LLM training and inference, computer vision, and time-series forecasting workloads. Topics span ray-train, ray-tune, ray-serve, and ray-data for distributed data processing, targeting practitioners who want to parallelize ML workloads across clusters.