In Part I for Azure MLOps for Begineers, we learnt how to train, register and deploy our model in Azure ML. In this part, we will learn how to use that endpoint to make forecast using a Flask Based web application. For basic Flask setup check : Streamlined Flask Deployment on Azure. We will build our forecasting application on the […]
Azure MLOps for Begineers: Train, Deploy and Serve a GRU forecasting model.
With Azure and GitHub CI/CD, you can automate your Flask deployment process for a seamless, instant launch. This powerful combination streamlines workflows, reduces manual effort, and ensures your web application is up and running in no time. In this guide, we’ll explore how to leverage GitHub CI/CD for rapid Flask deployment on Azure, empowering developers to deliver scalable, high-performance web apps with ease.
Breast Cancer Classification with Azure ML & MLflow: End-to-End ML Tutorial
Build an Image Classifier on Azure
Learn to train, register and deploy model in Azure ML
This introduction focuses on using MLflow Tracking to experiment with a Support Vector Machine (SVM) classifier on the Digits dataset, a classic dataset in machine learning for multi-class classification.