Introduction
AI applications require real-time communication between distributed services to deliver responsive, intelligent experiences. This module guides you through implementing event streaming and messaging with Azure Cache for Redis and Azure Managed Redis to enable asynchronous communication in your AI solutions.
Imagine you're a developer building an AI-powered document analysis platform for a legal tech company. Your system receives document uploads that must flow through multiple AI services: an OCR service extracts text, an NER service identifies entities, a classification service categorizes content, and an embedding service generates vectors for semantic search. Each analysis step takes several seconds, and you have hundreds of documents arriving simultaneously from multiple users. Without proper messaging infrastructure, you face impossible choices: either block API responses while all processing completes (creating unacceptable delays), or build complex polling mechanisms and retry logic (creating brittle, hard-to-maintain code). You also need to broadcast real-time status updates to user dashboards, coordinate work across multiple processing instances to avoid duplicate effort, and ensure no documents get lost if a service crashes mid-processing. The client expects sub-second API responses, automatic recovery from failures, and the ability to scale processing capacity dynamically as load increases. Without the right event streaming architecture, delivering a reliable, performant AI pipeline would require extensive custom infrastructure code.
After completing this module, you'll be able to:
- Explain Redis pub/sub messaging for broadcasting events to multiple AI services simultaneously
- Implement Redis Streams for reliable task queues with automatic retry and failure recovery
- Choose between pub/sub and Streams based on whether you need broadcast or coordinated work distribution
- Build Python applications that use pub/sub for notifications and Streams for processing pipelines
Note
All code examples in this module are based on the most recent version of the redis-py library at the time of writing. The library is updated often and the recommendation is to visit the redis-py GitHub site for the most up-to-date information.