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.

Additional Resources

MLOPs GitHub

MLOPs documentation

See MLOps in action

Build ML pipelines to design, deploy, and manage model workflows

Build ML pipelines to design, deploy, and manage model workflows

Deploy rapidly with confidence, using auto-scaling, managed, distributed inference clusters

Deploy rapidly with confidence, using auto-scaling, managed, distributed inference clusters

Integrate with Azure DevOps and GitHub Actions to automate ML workflows

Integrate with Azure DevOps and GitHub Actions to automate ML workflows

Create better governance and cost management across your ML projects

Create better governance and cost management across your ML projects

See how customers are delivering value with MLOps

"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."

Vijaya Sekhar Chennupati, Applied data scientist, Johnson Controls
Johnson Controls