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Mastering Model Context Protocol for Production AI
Chapters

1Foundations of Model Context Protocol

2Data Modeling for MCP

3Data Pipelines and MCP

4Context-Aware Prompt Engineering

5Model Selection and Architecture for MCP

6Evaluation, Testing, and Validation of MCP

Context relevance evaluationContext freshness metricsSafety and alignment testing for MCPBenchmark suites for context-rich tasksReliability and robustness testingRegression testing with context changesObservability metrics for MCPA/B testing with context variantsShadow testing and canary deploymentsFailure mode analysis for contextControllability and steerability testsDistortion and bias testing in contextPrivacy impact assessmentsCompliance testing for context dataEnd-to-end scenario testing

7Deployment Patterns and Runbooks

8Monitoring, Observability, and Reliability

9Privacy, Security, and Compliance in MCP

10Ethics, Governance, and Responsible AI in MCP

11Real-World Case Studies and Industry Applications

12Capstone Project and Roadmap

Courses/Mastering Model Context Protocol for Production AI/Evaluation, Testing, and Validation of MCP

Evaluation, Testing, and Validation of MCP

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Develop robust evaluation, testing, and validation practices to ensure MCP components meet safety, reliability, and compliance requirements.

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13 of 15

Privacy impact assessments

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