Machine learning operations (MLOps)
Azure Machine Learning capabilities that automate and accelerate the machine learning lifecycle
MLOps helps you deliver innovation faster
MLOps, or DevOps for machine learning, enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models.

Training reproducibility with advanced tracking of datasets, code, experiments, and environments in a rich model registry.
Autoscaling, powerful managed compute, no-code deploy, and tools for easy model training and deployment.
Efficient workflows with scheduling and management capabilities to build and deploy with continuous integration/continuous deployment (CI/CD).
Advanced capabilities to meet governance and control objectives and promote model transparency and fairness.
Resource center
Take a walk through of the end-to-end MLOps process.
Access videos and accompanying notebooks, code samples and documentation.
MLOps feature dive: Manage your assets, artifacts and code
MLOps feature dive: Create event driven machine learning workflows - Microsoft Channel 9 Video
See MLOps in action
See how customers are delivering value with MLOps
Sze-Wan Ng: Director of Analytics & Development, TransLink"With MLOps capabilities in Azure Machine Learning, we've improved bus departure predictions by 74 percent, and riders spend 50 percent less time waiting."

Vijaya Sekhar Chennupati, Applied data scientist, Johnson Controls"Using the MLOps capabilities in Azure Machine Learning, we were able to increase productivity and enhance operations, going to production in a timely fashion and creating a repeatable process."
