Practical skills for training and serving production-ready AI systems.
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Build shared understanding of AI across your organization with a single academy experience.
Anyscale Academy gives teams a curated route through Ray and production AI infrastructure, with enough structure to teach consistently and enough depth to support real implementation.
Learners build shared vocabulary around Ray Core, Data, Train, Serve, and production AI systems before jumping into isolated examples.
Courses connect concepts to runnable templates for batch inference, distributed training, serving, evaluation, and operational workflows.
Teams can track completion, certificates, bookmarks, streaks, and learning rank without turning enablement into spreadsheet archaeology.
Learning paths combine guided modules, checks, references, and examples so engineers can move from first principles to applied implementation.

Learning Path
The Ray 101 learning path is the perfect entry point to get you started with Ray. You'll learn about all of our libraries, from low-level distributed computing with Ray Core to running AI workloads with Ray Data, Train, Tune and Serve
We now host 22 courses spanning 48 modules and 299 lessons, from foundations to workload playbooks.

Foundations
This course helps admins deploy Anyscale clouds in custom environments (AWS vs. GCP, VMs vs. Kubernetes, etc.).

Foundations
Learn the foundations of distributed training of machine learning models with Ray Train.

Foundations
Learn the end-to-end multi-modal AI pipeline, including how each component fits together and what the project provides. You’ll also gain hands-on experience running and exploring the implementation using Anyscale or the GitHub repository.

Foundations
Explore the foundations of processing structured and unstructured data with Ray Data and get an overview of the key concepts and patterns for distributed data processing.