![]() You can get a free subscription to try it out. You need to have an Azure subscription. ![]() Here’s how you can set up Azure ML to follow the steps in this post. Throughout this post, I’ll assume you’re familiar with machine learning concepts like training and prediction, but I won’t assume familiarity with Azure. The code for this project can be found on GitHub. And finally, we’ll see how we can invoke the endpoint. After that, we’ll explore how we can create a batch endpoint on Azure, which will require the creation of several resources in the cloud. We’ll then write a scoring function that loads the model and performs predictions based on user input. We’ll start by getting familiar with our PyTorch model. In this post, I’ll show you how to work with batch endpoints. If you’re interested in managed online endpoints, check out my previous post. For more information about the different endpoint types and which one is right for you, check out the documentation. If you want to deploy an online endpoint, you have two options: Kubernetes online endpoints allow you to manage your own compute resources using Kubernetes, while managed online endpoints rely on Azure to manage compute resources, OS updates, scaling, and security. ![]() However, you get real-time responses, which is criticial to many scenarios. Compute resources are provisioned at the time of deployment, and are always up and running, which depending on your scenario may mean higher costs than batch endpoints. Online endpoints, on the other hand, are designed to quickly process smaller requests and provide near-immediate responses. However, that can result in substantially lower costs. Because compute resources are only provisioned when the job starts, the latency of the response is higher than using online endpoints. I’m going to focus on batch endpoints in this post, but let me start by explaining how the three types differ.īatch endpoints are designed to handle large requests, working asynchronously and generating results that are held in blob storage. This is the purpose of endpoints - they provide a simple web-based API for feeding data to your model and getting back inference results.Īzure ML currently supports three types of endpoints: batch endpoints, Kubernetes online endpoints, and managed online endpoints. Maybe you’re writing an application of your own that will rely on this service, or perhaps you want to make the service available to others. Suppose you’ve trained a machine learning model to accomplish some task, and you’d now like to provide that model’s inference capabilities as a service.
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