Ray

Ray's journey from a distributed computing framework to a crucial piece of AI infrastructure mirrors the evolution of AI system requirements. Its development showcases how computational demands have shaped the modern AI development landscape.
Ray

A powerful framework for scaling AI applications across multiple machines. It simplifies distributed computing for AI workloads.

Perfect for beginners who want to:

  • Scale AI applications efficiently
  • Run distributed AI workloads
  • Manage computational resources

Getting Started Tip: Begin with their single-machine examples before scaling to distributed systems.

Difficulty: ⭐⭐⭐⭐ (Advanced)

  • Distributed computing concepts required
  • Complex scaling patterns
  • Advanced Python knowledge needed
  • Multiple paradigms to understand

Visit Ray →
Basic Tutorial →

About the author
Surya

Surya

A technologist and an AI optimist keeping tabs on the development of AI agents and how it has societal impact along with the development.

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