This hands-on program equips AI practitioners to design, implement, and operate robust Model Context Protocol (MCP) in p...
Introduce MCP fundamentals, goals, and key terminology. Establish the mental model and success criteria for context-driven production AI.
Design robust context models with schemas, ontologies, and features that scale across deployment domains.
Build end-to-end data pipelines that reliably ingest, transform, and deliver contextual signals to models in real time or batch workflows.
Apply prompt engineering techniques that effectively fuse contextual signals into interactions, with safety and performance in mind.
Choose architectures and model families that maximize safe, scalable context usage and performance in production.
Develop robust evaluation, testing, and validation practices to ensure MCP components meet safety, reliability, and compliance requirements.
Establish safe, scalable deployment patterns and runbooks to release MCP-enabled systems with confidence and traceability.
Implement comprehensive monitoring, tracing, and reliability practices to detect, diagnose, and remediate MCP issues in production.
Protect user privacy and comply with regulatory requirements while enabling MCP capabilities in production environments.
Embed ethical considerations, governance structures, and responsible AI practices into MCP programs to protect users and society.
Explore real-world MCP deployments across industries to distill best practices, pitfalls, and measurable impact.
Apply everything learned in a structured capstone: scoping, design, implementation, validation, and deployment of an MCP-enabled solution.