Courses

Build the mental model for Ray tasks, actors, object references, scheduling, and resource-aware distributed Python.

Design Ray Data pipelines for parallel ingest, preprocessing, offline inference, and reliable outputs.

Coordinate distributed training jobs and credible hyperparameter experiments with Ray Train and Ray Tune.

Build Serve applications with deployment graphs, request routing, autoscaling, and LLM-ready controls.

Understand RLlib experiment structure, evaluation discipline, and policy checkpoint lifecycle.

Demonstrate readiness to design, review, and operate Ray workloads across Core, Data, Train, Tune, Serve, and RLlib.