Deploying Models at Scale on Azure - Part 1: Deploying XGBoost Models

Jarek Szczegielniak

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28 Mar 2022CPOL11 min read

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How to publish XGBoost and PyTorch models using Azure App Service, Flask, FastAPI, and machine learning online endpoints

This a Part 1 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 publish an XGBoost model trained to recognize handwritten digits from a well-known MNIST dataset. We use Azure App Service with Flask, then use machine learning online endpoints.

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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 1: Deploying XGBoost Models

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