We're delighted to welcome [Robert Clarke](https://www.linkedin.com/in/rjclarke7/), Principal Cloud Engineer, Cloudscaler and [Ryan Cormack](https://www.linkedin.com/in/ryancormack/), Software Engineer and Architect, Community Member Here's what they're bringing: Everyone's talking about serving giant LLMs - but what about serving hundreds of small models without paying to keep every one of them running? Robert worked on Cloudscaler's delivery of a production-ready AWS landing zone and inference pipeline for a large-scale clinical healthcare trial. Tonight Robert is walking us through that engagement end to end, then deep diving into Amazon SageMaker multi-model endpoints and the principles that made the platform production-ready. MCP, ACP, AG-UI, A2A, OpenTelemetry. Agents now run on open protocols, and Ryan builds the tooling that connects them, including strands-acp, which exposes Strands agents over the Agent Client Protocol, and acp-inspector for debugging ACP traffic. Tonight Ryan is showing us how to build agents with the open-source Strands Agents SDK, run them on Amazon Bedrock AgentCore Runtime and trace every step in Amazon CloudWatch. A big thank you to our sponsors [Cloudscaler](https://rebrand.ly/cloudscaler) & [Rayo](https://rebrand.ly/rayo-cloud) for making this event possible. Programme: 18:00: Arrival, registration 18:15: Talks start 20:00: Networking with food and a drink provided by the generosity of our sponsors. Session 1: *Physical AI in Surgical Logistics: Scaling Scalpel AI on Amazon SageMaker* with Robert Clarke Modern LLMs can be terabytes in size, needing clusters of GPUs, instances and hosts to run even a single copy. But what about the other end of the scale, when you have hundreds of small ML model variations you want to serve at the same time without spending thousands running them all hot? This session explores Amazon SageMaker multi-model endpoints in the context of an engagement run by Cloudscaler to deliver a production-rea
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