Mastering Model Context Protocol for Production AI

What you'll learn

• Design context schemas and model contextual signals across services
• Capture, store, and securely propagate context for multi-model systems
• Build reliable, scalable data pipelines for context ingestion and delivery
• Engineer context-aware prompts and integrate context into model inputs
• Select architectures that maximize safe and efficient context usage
• Validate and test MCP behavior with deterministic and probabilistic checks
• Deploy MCP-enabled systems with production runbooks and patterns
• Monitor observability, reliability, and drift for context flows
• Apply privacy, security, and compliance controls to contextual data
• Deliver an end-to-end MCP solution via a capstone project

Prerequisites

Working knowledge of Python, basic machine learning/LLM concepts, and familiarity with data engineering or production systems is required.

Skills you'll gain

• Context modeling
• Data engineering for ML
• Prompt engineering
• MLOps and deployment
• Observability and monitoring
• Validation and testing
• System architecture
• Privacy and security controls
• Governance and responsible AI
• Operational runbooks