If you’re running AI or ML workloads that don’t fit on one machine, this is for you.
Anyscale has launched a free Introduction to Ray course series: https://courses.anyscale.com/bundles/introduction-to-ray
Ray is an open source distributed computing framework for AI, used by teams at Cursor, Perplexity, Apple, xAI, and 1000s of others to scale model training, LLM fine-tuning, batch LLM inference, RAG pipelines, and multi-stage AI agents.
At your own pace, you’ll learn how to:
Scale regular Python functions with distributed tasks and actors
Run distributed training (PyTorch, TensorFlow, XGBoost) with Ray Train
Process large datasets with Ray Data
Serve models and build scalable AI services with Ray Serve
Ray gives you one Python-native substrate for data processing, training, tuning, and serving – across CPUs, GPUs, and heterogeneous clusters.
If you’d rather skip the theory and just try it:
You can get free credits to run Ray on Anyscale (a hosted Ray platform). No clusters to manage.
Spin up a Ray cluster, run a template, and see how it behaves at scale – no infra setup required.
*This offer is brought to you by the Anyscale team. We appreciate their work on Ray and the open source tooling that helps teams run production AI at scale, and their support of Turing Post’s mission to bring clarity to the AI landscape.
