Summary
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In this module, you learned how Azure Functions provides a serverless compute platform for building AI backends that scale automatically with demand and charge only for actual execution time. You explored the differences between the Flex Consumption, Premium, and Consumption hosting plans, and evaluated cold start behavior and scaling mechanics for latency-sensitive AI inference endpoints. You also set up a local development environment using Azure Functions Core Tools and the Azurite storage emulator to build and debug functions before deploying to Azure. You created HTTP triggers for inference endpoints and queue triggers for asynchronous batch processing, and used output bindings to write results to Azure Storage and Cosmos DB without writing boilerplate connection code. Additionally, you configured application settings, Key Vault references, and Azure App Configuration to manage secrets and configuration securely across environments. Finally, you applied managed identity to authenticate your function app to downstream Azure services without storing credentials, and secured HTTP endpoints using function-level authorization keys.