Building a Python Web Service with Ray
summary
Shows how to integrate Python web frameworks with Ray to build scalable web services, using Ray tasks and actors to manage compute and state. Covers practical engineering details—writing async code, type checking, testing—and deployment concerns such as monitoring and distributed tracing, illustrated with Anyscale’s architecture and technology choices.
AI-written summary · about
chapters (6)
- 0:00Introduction to Ray
- 3:06Building scalable web services
- 7:25Monitoring and deployment
- 11:51Case study and examples
- 15:40Testing and performance metrics
- 18:12Summary and deployment
related talks
24mRay: A System for High-performance, Distributed ML Applications
15mRay: A Distributed Compute Engine for AI
11mKeynote Q&A
14mBuilding High Availability and Scalability Online Computing Applications on Ray
17mScaling and Unifying SciKit Learn and Spark Pipelines using Ray
21mPowering Open Data Hub with Ray
23mImproving Ray for Large-scale Applications
03mRay: A Distributed Execution Framework for Emerging AI Applications
