Ray Online Courses for Distributed AI & ML*

2026-02-26 · Source: Turing Post · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

Anyscale has launched a free online course series titled "Introduction to Ray," designed for professionals scaling AI/ML workloads beyond single machines. Ray is an open-source distributed computing framework for AI, utilized by companies like Cursor, Perplexity, Apple, and xAI for tasks such as model training, LLM fine-tuning, batch LLM inference, RAG pipelines, and multi-stage AI agents. The self-paced course teaches users to scale Python functions with distributed tasks and actors, perform distributed training with Ray Train (supporting PyTorch, TensorFlow, XGBoost), process large datasets using Ray Data, and build scalable AI services with Ray Serve. Ray provides a unified Python-native platform for data processing, training, tuning, and serving across diverse hardware like CPUs and GPUs.

Key takeaway

For ML engineers struggling to scale AI/ML workloads on single machines, exploring Anyscale's free "Introduction to Ray" course can provide practical skills. You can learn to distribute Python functions, train models, process data, and serve AI services across clusters. Additionally, you can get free credits to experiment with Ray on Anyscale's hosted platform, bypassing infrastructure setup.

Key insights

Ray is an open-source distributed computing framework for scaling AI/ML workloads across heterogeneous clusters.

Principles

• Unify AI lifecycle stages on one substrate.
• Scale Python functions with distributed primitives.

Method

Ray enables distributed tasks, actors, data processing (Ray Data), model training (Ray Train), and model serving (Ray Serve) for scalable AI applications.

In practice

• Use Ray Train for distributed PyTorch/TensorFlow.
• Implement Ray Serve for scalable AI services.
• Process large datasets with Ray Data.

Topics

• Ray Framework
• Distributed AI/ML
• LLM Fine-tuning
• Model Serving
• Distributed Training

Best for: NLP Engineer, Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Turing Post.