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
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Understand the different model deployments and their use cases
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Observe how to deploy a model to a real-time online endpoint
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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:
Hallam is a software architect with over 20 years experience across a wide range of industries. He began his software career as a Delphi/Interbase disciple but changed his allegiance to Microsoft with its deep and broad ecosystem. While Hallam has designed and crafted custom software utilizing web, mobile and desktop technologies, good quality reliable data is the key to a successful solution. The challenge of quickly turning data into useful information for digestion by humans and machines has led Hallam to specialize in database design and process automation. Showing customers how leverage new technology to change and improve their business processes is one of the key drivers keeping Hallam coming back to the keyboard.