Develop AI solutions with Azure Database for PostgreSQL
This learning path guides you through developing AI solutions using Azure Database for PostgreSQL: building a data foundation with schema design, SQL queries, and secure Python integration via Microsoft Entra authentication; implementing vector search with pgvector for embeddings and RAG retrieval patterns; and optimizing vector search performance through tuning, indexing, data layout, scaling, and connection pooling.
Modules
Expand each module and click a topic to open detailed study notes.
Build and query with Azure Database for PostgreSQLLearn 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.
See more
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.
See moreLearn 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 PostgreSQLLearn 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.
See more
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.
See moreLearn 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 PostgreSQLLearn 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.
See more
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.
See moreLearn 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.