Deploying Models in Azure Machine Learning

Deploying Models in Azure Machine Learning explains how to make trained models available to end users for inference. The lesson starts with an overview describing the types of model deployment and their constituent elements. This is followed by demonstrations of each deployment type, one via Azure ML Studio and one using the Python SDK.

Learning Objectives

• Understand the different model deployments and their use cases

• Observe how to deploy a model to a real-time online endpoint

• Learn how to deploy a model for batch processing

Intended Audience

Anyone who wants to know learn to deploy trained models within Azure Machine Learning.

Prerequisites

To get the most out of this lesson, you should have some knowledge of Azure Machine Learning. We recommend the following two lessons:

• Creating Models in Azure Machine Learning Studio Designer

• Implementing Pipelines in Azure Machine Learning