This Azure MLOps course provides a complete end-to-end introduction to building and deploying machine learning systems using Microsoft Azure. It focuses on integrating DevOps practices with machine learning workflows to create scalable and production-ready ML pipelines.
The course begins with an introduction to Azure Machine Learning Service and explains how it fits into modern MLOps architectures. You will also learn the basics of Azure DevOps and how it supports automation in ML projects.
As the course progresses, it covers configuration setup, infrastructure as code, and how to build automated pipelines for machine learning workflows. These pipelines ensure consistent and reproducible environments for training and deployment.
A major focus of the course is Continuous Integration (CI), where you learn how to automatically train and validate ML models as part of a pipeline. This ensures that every change in data or code is properly tested before deployment.
The course then moves into Continuous Deployment (CD), showing how to deploy models into staging and production environments using Azure pipelines. You will also learn how to test full end-to-end MLOps systems to ensure reliability.
The final section includes a fast-track MLOps V2 accelerator, demonstrating how to deploy a complete MLOps system in under an hour.
By the end of this course, you will understand how to build, automate, and deploy machine learning systems using Azure DevOps and Azure Machine Learning.
