This repository contains examples of how to implement MLOps using Azure ML Services and Azure DevOps.

MLOps (a compound of "machine learning" and "operations") is a practice for collaboration and communication between data scientists and operations professionals to help manage production ML lifecycle. Similar to the DevOps or DataOps approaches, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements.

Azure ML contains a number of asset management and orchestration services to help you manage the lifecycle of your model training & deployment workflows.

With Azure ML + Azure DevOps you can effectively and cohesively manage your datasets, experiments, models, and ML-infused applications.