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Mastering Model Context Protocol for Production AI
Chapters

1Foundations of Model Context Protocol

2Data Modeling for MCP

Context schema designOntologies and taxonomiesEntity relationships in production AIFeature stores and context featuresTemporal aspects of contextContext indexing and retrievalData normalization for contextHandling missing context dataData quality metrics for contextVersioned context catalogsMetadata and lineage captureData privacy constraints in contextsAnonymization techniques for contextual dataSynthetic context generationMetadata-driven governance

3Data Pipelines and MCP

4Context-Aware Prompt Engineering

5Model Selection and Architecture for MCP

6Evaluation, Testing, and Validation of MCP

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/Data Modeling for MCP

Data Modeling for MCP

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Design robust context models with schemas, ontologies, and features that scale across deployment domains.

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12 of 15

Data privacy constraints in contexts

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