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Using AI Features in SQL Server 2025
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Who is this course for?
Who is Greg?
What will I learn in this course?
Module introduction
Quiz 1
Module introduction
Quiz 2
Module introduction
Quiz 3
Module introduction
Quiz 4
Module introduction
Quiz 5
Module introduction
Managing versioning and re-embedding strategies
Quiz 6
Module introduction
Quiz 7
Module introduction
Cost and throttling implications
Quiz 8
Module introduction
What RAG means in practical terms
Designing SQL tables for retrieval
Executing similarity search as part of a broader workflow
Passing retrieved data to AI services
Limitations of RAG when SQL Server is the system of record
Lab 9 video walkthroughs
Lab 9
Quiz 9
Module introduction
Protecting sensitive data used in AI workflows
Controlling access to vector data
Auditing AI-related queries
Managing secrets and credentials
Operational risks and failure modes
Lab 10 video walkthroughs
Lab 10
Quiz 10
Module introduction
Scenarios where SQL Server is the wrong tool
When a separate vector database makes more sense
When Fabric or other platforms are more appropriate
Cost, complexity, and maintenance trade-offs
Decision checklist for architects and DBAs
Lab 11 video walkthroughs
Lab 11
Quiz 11
Module introduction
End-to-end reference architecture
Design review of a realistic use case
Common pitfalls seen in early implementations
How to evolve solutions over time without re-architecture
Lab 12 video walkthroughs
Lab 12
Quiz 12
Summary and further steps
Module 0: Getting started
Who is this course for?
Preview
Who is Greg?
Preview
What will I learn in this course?
Preview
Module 1: AI in the context of SQL Server
Module introduction
Quiz 1
Module 2: Vector data and embeddings fundamentals
Module introduction
Quiz 2
Module 3: Vector data types in SQL Server
Module introduction
Quiz 3
Module 4: Querying vector data
Module introduction
Quiz 4
Module 5: Vector indexing and performance
Module introduction
Quiz 5
Module 6: Generating embeddings outside SQL Server
Module introduction
Managing versioning and re-embedding strategies
Quiz 6
Module 7: REST and HTTP basics
Module introduction
Quiz 7
Module 8: Calling AI services from SQL Server
Module introduction
Cost and throttling implications
Quiz 8
Module 9: Retrieval-augmented query patterns
Module introduction
What RAG means in practical terms
Designing SQL tables for retrieval
Executing similarity search as part of a broader workflow
Passing retrieved data to AI services
Limitations of RAG when SQL Server is the system of record
Lab 9 video walkthroughs
Lab 9
Quiz 9
Module 10: Security, governance, and operational concerns
Module introduction
Protecting sensitive data used in AI workflows
Controlling access to vector data
Auditing AI-related queries
Managing secrets and credentials
Operational risks and failure modes
Lab 10 video walkthroughs
Lab 10
Quiz 10
Module 11: When NOT to use AI features in SQL Server
Module introduction
Scenarios where SQL Server is the wrong tool
When a separate vector database makes more sense
When Fabric or other platforms are more appropriate
Cost, complexity, and maintenance trade-offs
Decision checklist for architects and DBAs
Lab 11 video walkthroughs
Lab 11
Quiz 11
Module 12: Putting it all together
Module introduction
End-to-end reference architecture
Design review of a realistic use case
Common pitfalls seen in early implementations
How to evolve solutions over time without re-architecture
Lab 12 video walkthroughs
Lab 12
Quiz 12
Module 13: Next steps
Summary and further steps
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