Deploying Models at Scale on Azure - Part 3: Deploying and Scaling TensorFlow Models

Jarek Szczegielniak

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30 Mar 2022CPOL10 min read

How to publish a TensorFlow model

This a Part 3 of a 3-part series of articles that demonstrate how to take AI models built using various Python AI frameworks and deploy and scale them using Azure ML Managed Endpoints. In this article, we use online endpoints to publish a TensorFlow model. Then, we create an Azure Function as a public proxy to this endpoint. In addition, we explore configuration options for managed endpoints, such as autoscaling and the blue-green deployment concept.

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This article is part of the series 'Deploying Models at Scale View All

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This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

Written By

Jarek Szczegielniak

Architect

Poland

Jarek has two decades of professional experience in software architecture and development, machine learning, business and system analysis, logistics, and business process optimization.
He is passionate about creating software solutions with complex logic, especially with the application of AI.

Deploying Models at Scale on Azure - Part 3: Deploying and Scaling TensorFlow Models

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