Develop AI solutions with Azure Database for PostgreSQL

  • Learning Path
  • 3 Modules

At a glance

This learning path guides you through developing AI solutions using Azure Database for PostgreSQL. You start by building a data foundation with schema design, efficient SQL queries, and secure Python integration using Microsoft Entra authentication.

You then implement vector search using the pgvector extension to store embeddings, execute similarity searches with different distance metrics, and build retrieval patterns that integrate with RAG pipelines for semantic search and recommendations.

Finally, you optimize vector search performance by tuning PostgreSQL and pgvector configuration, selecting appropriate vector indexes, designing efficient data layouts, scaling for high-volume workloads, and implementing connection pooling for AI applications.

Prerequisites

  • Programming experience with Python.
  • Basic understanding of Azure services and cloud computing concepts.
  • Familiarity with relational databases and SQL fundamentals.
  • Understanding of machine learning concepts including embeddings and similarity search.

Modules in this learning path

Build and query with Azure Database for PostgreSQL

Learn how to use Azure Database for PostgreSQL to build data foundations for AI applications. Design schemas, write efficient queries, and integrate with Python applications using secure authentication.

Implement vector search with Azure Database for PostgreSQL

Learn how to implement vector search using the pgvector extension in Azure Database for PostgreSQL. Store embeddings, create vector indexes, and build semantic retrieval patterns for AI applications.

Optimize vector search in Azure Database for PostgreSQL

Learn how to optimize vector search performance in Azure Database for PostgreSQL using pgvector. Tune configuration parameters, select and configure vector indexes, design efficient data layouts, scale for high-volume workloads, and implement connection pooling for AI applications.

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