Ray has emerged as the leading distributed compute framework for abstracting the complexity of the evolving landscape of AI frameworks. It offers a unified way to scale data processing, training, and inference from a laptop to multi-node GPU+CPU clusters.
But power doesn’t always mean agility or efficiency. Running Ray in production requires expertise, and managing it yourself can slow progress and inflate engineering costs.
Built by the creators of Ray, Anyscale delivers a fully managed platform that helps you take your AI ambitions to production faster and easier.
AI workload processing challenges and how Ray helps
How Anyscale makes Ray production-ready
A live demo of Anyscale’s advanced features
AI engineers, ML practitioners, and platform teams evaluating Ray or looking for a simpler, fully managed solution to scale AI workloads efficiently in production.
**We have an EMEA time session on Thursday, July 31st at 3pm CEST / 2pm BST.**