Integrate backend services for AI solutions

  • Learning Path
  • 3 Modules

At a glance

This learning path teaches you how to build and integrate backend services that support AI solutions on Azure. You start by using Azure Service Bus to decouple AI application components, queue inference requests, and process messages reliably with queues, topics, and dead-letter queues. You then build event-driven workflows with Azure Event Grid to route events between services with low latency, configure delivery policies, and publish custom events from AI applications. Finally, you create serverless AI backends with Azure Functions, including inference endpoints, event processors, and secure integrations with other Azure services.

Prerequisites

  • Programming experience with Python.
  • Basic understanding of Azure services and cloud computing concepts.
  • Familiarity with distributed systems concepts and event-driven architectures.
  • Familiarity with REST APIs and asynchronous messaging patterns.

Modules in this learning path

Queue and process AI operations with 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.

Develop event-driven AI workflows with Azure Event Grid

Build reactive AI architectures using Azure Event Grid to route events from sources to handlers with low latency and high reliability. Learn to configure event subscriptions, design CloudEvents, implement delivery policies, and publish custom events from AI applications.

Build serverless AI backends with Azure Functions

Learn how to use Azure Functions as lightweight serverless compute for AI workloads. Build inference endpoints, event processors, and service integrations that scale automatically with demand.

Start