Queue and process AI operations with Azure Service Bus
Intermediate
Developer
Azure Service Bus
Learn how to use Azure Service Bus to decouple AI application components, queue inference requests, distribute processing workloads across competing consumers, and handle failures through dead-letter queues. This module covers queues, topics with subscriptions, message structuring for AI payloads, and reliable message processing with the Python SDK.
Learning objectives
After completing this module, you'll be able to:
- Explain how Azure Service Bus decouples AI application components and identify when to apply messaging patterns such as load leveling, competing consumers, and publish-subscribe.
- Choose between Service Bus queues and topics with subscriptions based on whether an AI workflow requires single-consumer processing or fan-out to multiple consumers.
- Structure Service Bus messages for AI workloads, including serializing prompts and model parameters, handling large payloads with the claim-check pattern, and including correlation IDs for end-to-end request tracking.
- Process messages reliably using peek-lock receive mode, handle poison messages through dead-letter queues, and monitor the dead-letter queue for failed inferences.
Prerequisites
Before beginning this module, you should have:
- Programming experience with languages such as Python, JavaScript, or C#.
- Basic understanding of Azure services and cloud computing concepts.
- Familiarity with asynchronous messaging concepts and distributed systems patterns.