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- Learn how to use Azure Service Bus to queue, distribute, and reliably process AI workloads using queues, topics, subscriptions, and dead-letter queues.
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.
- Learn how to build event-driven AI workflows using Azure Event Grid to route events from sources to handlers with low latency, configure delivery policies, and publish custom events.
After completing this module, you'll be able to:
- Explain how Azure Event Grid enables event-driven patterns in AI solutions and identify the core components (topics, event subscriptions, and event handlers) that form an event-routing architecture.
- Design events using the CloudEvents schema for AI operations, define custom event types, and configure event subscriptions with filters that route events based on type, subject, or data attributes.
- Configure delivery and retry policies to handle transient failures in AI pipelines, set dead-letter destinations for undeliverable events, and monitor delivery outcomes.
- Publish custom events from AI applications to signal completed inferences, model updates, or pipeline stage transitions using the Event Grid SDK and REST API.
- Learn how to use Azure Functions as lightweight serverless compute for AI workloads, including inference endpoints, event processors, and integration with Azure services.
After completing this module, you'll be able to:
- Evaluate cold start, scaling, and instance memory trade-offs when choosing between Flex Consumption and Premium hosting for AI workloads
- Set up a local development environment for Azure Functions using Core Tools, emulators, and an IDE
- Create triggers and bindings that implement common AI integration patterns such as HTTP inference endpoints and queue-based batch processors
- Configure secrets management and application settings using Key Vault references and Azure App Configuration
- Apply managed identity and function-level authorization to secure access between Functions and other Azure resources
