Agentic systems are powerful – but scaling them can be painful. In the first part of our webinar series, we introduced the Model Context Protocol (MCP) – an open, model-agnostic interface that simplifies tool integration for agentic systems.
But what happens when your prototype needs to serve real users at scale?
This session dives into exactly that: how to deploy MCP using Ray Serve and Anyscale. We’ll cover the architectural shift from single-process experiments to distributed, production-grade services – and walk through live demos that show how it's done.
You’ll leave this webinar with a clear blueprint for building scalable, fault-tolerant AI systems that are ready for production.
We’ll Cover:
-How Ray Serve solves scaling limitations of traditional MCP deployments
-Deployment architectures for multi-service MCP setups
-Four real-world demos on how to deploy MCP with Ray Serve for Streamable HTTP and STDIO transports
-How to deploy MCP reliably on Anyscale
-Built-in features for logging, monitoring, and scaling
Who It’s For:
Anyone designing or operating AI agent systems – especially if you’re facing integration pain, scale challenges, or production pressure.