![]() ![]() Managed online endpoints work with powerful CPU and GPU machines in Azure in a scalable, fully managed way. Managed online endpoints help to deploy your ML models in a turnkey manner. For more information on endpoints and differences between managed online endpoints and Kubernetes online endpoints, see What are Azure Machine Learning endpoints?. There are two types of online endpoints: managed online endpoints and Kubernetes online endpoints. Online endpoints are endpoints that are used for real-time inferencing. You start with a model and end up with a scalable HTTPS/REST endpoint that you can use for real-time scoring. You'll also learn how to view the logs and monitor the service-level agreement (SLA). ![]() You'll begin by deploying a model on your local machine to debug any errors, and then you'll deploy and test it in Azure. Learn how to use an online endpoint to deploy your model, so you don't have to create and manage the underlying infrastructure. ![]() APPLIES TO: Azure CLI ml extension v2 (current) Python SDK azure-ai-ml v2 (current) ![]()
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