AI-3019: Build AI Apps with Azure Database for PostgreSQL

The Build AI Apps with Azure Database for PostgreSQL (AI-3019) course is a 1-day training program focused on building AI-powered applications with PostgreSQL. Participants will learn vector search, pgvector, Azure OpenAI integration, and RAG patterns using Azure Database for PostgreSQL. Delivered by Esamatic srl, Microsoft Learning Partner in Milan, with Microsoft Certified Trainers.

• Duration: 1 day of instructor-led training
• Focus: AI-powered applications with PostgreSQL on Azure
• Platform: Azure Database for PostgreSQL, Azure OpenAI
• Approach: Hands-on labs with vector search and RAG patterns
• Certification: Supports Azure AI and data platform certifications

Course Overview: Build AI Apps with Azure Database for PostgreSQL

PostgreSQL has emerged as a leading database for AI applications thanks to its extensibility and the pgvector extension for vector similarity search. This course teaches participants to combine Azure Database for PostgreSQL with Azure OpenAI to build intelligent applications that leverage vector embeddings, semantic search, and RAG (Retrieval-Augmented Generation) patterns for grounded, accurate AI responses.

Learning Objectives

1. Vector Search Fundamentals — Understand and implement vector similarity search using pgvector in Azure Database for PostgreSQL
2. Embeddings & Indexing — Generate vector embeddings with Azure OpenAI and create efficient indexes for high-performance semantic search
3. RAG Patterns — Build Retrieval-Augmented Generation pipelines that combine PostgreSQL vector search with Azure OpenAI for accurate, grounded responses
4. Application Integration — Integrate vector search and AI capabilities into production applications with proper connection management and scaling

Who Should Attend

This course is ideal for backend developers, database administrators, AI engineers, and full-stack developers who want to build AI-powered applications using PostgreSQL on Azure.

Career Benefits

Vector databases and RAG architectures are fundamental to modern AI applications. Professionals who can combine database expertise with AI integration skills are highly sought after as organizations build knowledge-grounded AI solutions.

Prerequisites

• Experience with PostgreSQL or relational databases
• Basic programming skills (Python preferred)
• General understanding of AI and embeddings concepts

Frequently Asked Questions

Do I need prior experience with vector databases?

No. The course introduces vector search concepts from the ground up, including what embeddings are, how vector similarity works, and how pgvector extends PostgreSQL with these capabilities.

Why use PostgreSQL for AI instead of a dedicated vector database?

PostgreSQL with pgvector allows organizations to add vector search capabilities to their existing PostgreSQL infrastructure, avoiding the complexity of managing a separate vector database while leveraging PostgreSQL’s mature ecosystem.

Does the course cover production deployment considerations?

Yes. The course includes guidance on indexing strategies, query optimization, connection pooling, and scaling patterns for production AI applications on Azure Database for PostgreSQL.

What types of AI applications will I build?

You will build semantic search applications, question-answering systems using RAG, and recommendation engines that combine vector similarity with traditional SQL queries.