The speakers discuss transitioning AI agents from local FastAPI prototypes to resilient production systems on Kubernetes using Ray. They share operational insights on RayCluster topology, stateful memory handling, auto-scaling on dynamic traffic, and robust control plane designs, while highlighting critical guardrails, multi-region setups, and LLM-as-a-judge evaluation pipelines. By Deepak Mohanakumar Chandramouli, Bhumik Vinodkumar Thakkar

Owners say ordinary conversations are unexpectedly appearing in Meta AI history.

The Azure Kubernetes Service (AKS) team at Microsoft has shared guidance for running Anyscale's managed Ray service at scale. They focus on three key issues: GPU capacity limits, scattered ML storage, and problems with credential expiry. By Claudio Masolo

Utilizing feature stores like Feast and distributed compute frameworks like Ray in production machine learning systems