Master the basics of distributed computing with this foundations course on Ray Core.
Learn the fundamentals of Ray Core by turning Python functions into distributed tasks with `@ray.remote`, executing them in parallel with `.remote()`, and retrieving results using `ray.get`. You’ll practice by writing and running your own remote task and learn a key anti-pattern to avoid (`ray.get` in a loop) to preserve parallelism.
In this Advanced module, you’ll learn Ray Core’s fundamental building blocks—object store, tasks, and actors—and how to use `ObjectRef`s to efficiently share large data and chain distributed computations without unnecessary `ray.get()` calls. You’ll practice key patterns (and avoid common anti-patterns) for building scalable execution graphs locally or on a Ray cluster.
