Database Inferencing with Azure Machine Learning and PostgreSQL

Modern applications require real-time predictions, but moving data back and forth between databases and machine learning servers introduces latency and complexity. Integrating machine learning models directly with your relational database solves this bottleneck. This text-based course guides you through the process of connecting Azure Database for PostgreSQL with Azure Machine Learning. You will learn to register models, set up secure connections, and execute predictive queries directly using SQL. What you will learn: Understand the fundamentals of database-level machine learning inferencing; Configure the connection between Azure Database for PostgreSQL and Azure Machine Learning; Deploy machine learning models to managed endpoints for database consumption; Write SQL queries that call machine learning models to make real-time predictions; Apply modern vector search techniques using pgvector for advanced semantic queries; Secure your database-to-machine-learning pipeline using best practices for access control. Starting with basic architecture and terminology, this course walks you through the step-by-step logic of model deployment, SQL extension configuration, and query optimization. You will practice through written scenarios and structured SQL examples. Designed for database administrators, data engineers, and backend developers new to cloud-based machine learning integrations, this course requires no prior machine learning experience, though a basic understanding of SQL is helpful. Start reading today to unlock the power of intelligent SQL queries.