Deploying Azure Machine Learning service models for inference with Azure Functions

This article shows how to deploy an Azure Machine Learning service (AML) generated model to an Azure Function. Right now, AML supports a variety of choices to deploy models for inferencing – GPUs, FPGA, IoT Edge, custom Docker images. Customers have provided feedback to support – an event-driven serverless compute platform that can also solve complex orchestration problems – as a model deployment endpoint within Azure Machine Learning service, and our AI platform engineering team is looking into this feedback to make it a seamless experience. In the meantime, this blog post will walk you through the set of steps to manually download the model file and package it as part of Azure Functions for inferencing . This article uses Python code to build an E2E ML model and Azure functions.