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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?
Configuring your lab environment
Module introduction
AI features in SQL Server 2025
What is new in SQL Server 2025
What SQL Server is responsible for
What SQL Server is not responsible for
Misconceptions to avoid
Positioning SQL Server correctly
Lab 1
Lab 1 video walkthroughs
Quiz 1
Module introduction
What is an AI model?
What embeddings are and why they exist
How vector similarity differs from relational comparison
How SQL Server uses vector similarity
Typical AI scenarios that involve vectors
Why vectors work well for these scenarios
When vectors are inappropriate
How this applies to SQL Server
How to think about vectors going forward
Where embeddings models are hosted
Common embeddings models
Lab 2
Lab 2 video walkthroughs
Quiz 2
Module introduction
Vector data type basics
Declaring VECTOR columns and variables
How VECTOR values are represented
Embeddings as derived data
Practical table pattern for embeddings
Vectors complement relational data
Parallel tables and schema evolution
Dimensionality is enforced
Dimensionality changes are migration events
Storage characteristics of VECTOR
float16 vectors
VECTOR limitations in SQL Server
Lab 3
Lab 3 solution
Lab 3 video walkthroughs
Quiz 3
Module introduction
What similarity search means in T-SQL
Exact similarity vs approximate search
Writing similarity queries in T-SQL
Using VECTOR_DISTANCE
Combining vector similarity with relational predicates
Using VECTOR_SEARCH
Common query mistakes and inefficiencies
How to reason about vector queries in T-SQL
Using VECTOR_NORM
Using VECTOR_NORMALIZE
Using VECTORPROPERTY
Lab 4
Lab 4 solution
Lab 4 video walkthroughs
Quiz 4
Module introduction
Why vector indexing exists
Vector index characteristics
Operational constraints of vector indexes
Creating a vector index
Accuracy versus performance trade-offs
CPU and memory impact of vector queries
Monitoring and diagnosing vector query performance
When vector indexes should not be used
Lab 5
Lab 5 solution
Lab 5 video walkthroughs
Quiz 5
Module introduction
Why SQL Server does not generate embeddings
Common embedding generation patterns
Storing embeddings safely in SQL Server
Refreshing and regenerating embeddings
Handling changes in embedding models
Versioning strategies for embeddings
Re-embedding strategies at scale
Quiz 6
Module introduction
What REST means in practice
Core HTTP concepts
JSON as a data format
Authentication at a conceptual level
Error handling and diagnostics
How REST concepts apply to SQL Server integrations
What SQL Server professionals need to know
Using Caddy as a proxy
Testing the Caddy proxy
Lab 7
Lab 7 solution
Lab 7 video walkthroughs
Quiz 7
Module introduction
Synchronous vs asynchronous calls
Patterns for invoking external AI services
Security considerations for outbound calls
Cost and throttling implications
Operational risks and failure modes
Designing SQL Server–safe AI integrations
Using sp_invoke_external_rest_endpoint
Calling sp_invoke_external_rest_endpoint
Minimal structure of a REST call in T-SQL
Retrieving text embeddings via SQL Server REST
Using CREATE EXTERNAL MODEL
Using AI_GENERATE_EMBEDDINGS
Handling REST authentication
Supported external model types
Lab 8
Lab 8 solution
Lab 8 video walkthroughs
Quiz 8
Module introduction
What RAG means in practical terms
Designing SQL tables for retrieval
Executing similarity search as part of a workflow
Passing retrieved data to AI services
Limitations of RAG for SQL Server
Failure modes in SQL Server–based RAG
How to use SQL Server effectively in RAG
Lab 9
Lab 9 solution
Lab 9 video walkthroughs
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 introduced by AI features
Governance boundaries in AI-enabled systems
How to approach AI governance with SQL Server
Quiz 10
Module introduction
Why it matters to say no
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
Quiz 11
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
Configuring your lab environment
Module 1: AI in the context of SQL Server
Module introduction
AI features in SQL Server 2025
What is new in SQL Server 2025
What SQL Server is responsible for
What SQL Server is not responsible for
Misconceptions to avoid
Positioning SQL Server correctly
Lab 1
Lab 1 video walkthroughs
Quiz 1
Module 2: Vector data and embeddings fundamentals
Module introduction
What is an AI model?
What embeddings are and why they exist
How vector similarity differs from relational comparison
How SQL Server uses vector similarity
Typical AI scenarios that involve vectors
Why vectors work well for these scenarios
When vectors are inappropriate
How this applies to SQL Server
How to think about vectors going forward
Where embeddings models are hosted
Common embeddings models
Lab 2
Lab 2 video walkthroughs
Quiz 2
Module 3: Vector data types in SQL Server
Module introduction
Vector data type basics
Declaring VECTOR columns and variables
How VECTOR values are represented
Embeddings as derived data
Practical table pattern for embeddings
Vectors complement relational data
Parallel tables and schema evolution
Dimensionality is enforced
Dimensionality changes are migration events
Storage characteristics of VECTOR
float16 vectors
VECTOR limitations in SQL Server
Lab 3
Lab 3 solution
Lab 3 video walkthroughs
Quiz 3
Module 4: Querying vector data
Module introduction
What similarity search means in T-SQL
Exact similarity vs approximate search
Writing similarity queries in T-SQL
Using VECTOR_DISTANCE
Combining vector similarity with relational predicates
Using VECTOR_SEARCH
Common query mistakes and inefficiencies
How to reason about vector queries in T-SQL
Using VECTOR_NORM
Using VECTOR_NORMALIZE
Using VECTORPROPERTY
Lab 4
Lab 4 solution
Lab 4 video walkthroughs
Quiz 4
Module 5: Vector indexing and performance
Module introduction
Why vector indexing exists
Vector index characteristics
Operational constraints of vector indexes
Creating a vector index
Accuracy versus performance trade-offs
CPU and memory impact of vector queries
Monitoring and diagnosing vector query performance
When vector indexes should not be used
Lab 5
Lab 5 solution
Lab 5 video walkthroughs
Quiz 5
Module 6: Generating embeddings outside SQL Server
Module introduction
Why SQL Server does not generate embeddings
Common embedding generation patterns
Storing embeddings safely in SQL Server
Refreshing and regenerating embeddings
Handling changes in embedding models
Versioning strategies for embeddings
Re-embedding strategies at scale
Quiz 6
Module 7: REST and HTTP basics
Module introduction
What REST means in practice
Core HTTP concepts
JSON as a data format
Authentication at a conceptual level
Error handling and diagnostics
How REST concepts apply to SQL Server integrations
What SQL Server professionals need to know
Using Caddy as a proxy
Testing the Caddy proxy
Lab 7
Lab 7 solution
Lab 7 video walkthroughs
Quiz 7
Module 8: Calling AI services from SQL Server
Module introduction
Synchronous vs asynchronous calls
Patterns for invoking external AI services
Security considerations for outbound calls
Cost and throttling implications
Operational risks and failure modes
Designing SQL Server–safe AI integrations
Using sp_invoke_external_rest_endpoint
Calling sp_invoke_external_rest_endpoint
Minimal structure of a REST call in T-SQL
Retrieving text embeddings via SQL Server REST
Using CREATE EXTERNAL MODEL
Using AI_GENERATE_EMBEDDINGS
Handling REST authentication
Supported external model types
Lab 8
Lab 8 solution
Lab 8 video walkthroughs
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 workflow
Passing retrieved data to AI services
Limitations of RAG for SQL Server
Failure modes in SQL Server–based RAG
How to use SQL Server effectively in RAG
Lab 9
Lab 9 solution
Lab 9 video walkthroughs
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 introduced by AI features
Governance boundaries in AI-enabled systems
How to approach AI governance with SQL Server
Quiz 10
Module 11: When NOT to use AI features in SQL Server
Module introduction
Why it matters to say no
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
Quiz 11
Module 12: Next steps
Summary and further steps
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