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Machine-learning operations (MLOps)

Azure Machine Learning capabilities that automate and accelerate the machine learning lifecycle

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.

Deliver innovation rapidly

MLOps—machine learning operations, 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.

Build reproducible workflows and models

Reduce variations in model iterations and provide fault tolerance for enterprise-grade scenarios through reproducible training and models. Use datasets and rich model registries to track assets. Enable enhanced traceability with tracking for code, data, and metrics in run history. Build machine learning pipelines to design, deploy, and manage reproducible model workflows for consistent model delivery.

Easily deploy highly accurate models anywhere

Deploy rapidly with confidence. Use autoscaling, managed CPU, and GPU clusters with distributed training in the cloud. Package models quickly and ensure high quality at every step using model profiling and validation tools. Use controlled rollout to promote models into production.

Efficiently manage the entire machine learning lifecycle

Use built-in integration with Azure DevOps and GitHub Actions for seamlessly scheduling, managing, and automating workflows. Optimize model training and deployment pipelines, build for CI/CD to facilitate retraining, and easily fit machine learning into your existing release processes. Use advanced data-drift analysis to improve model performance over time.

Achieve governance across assets

Track model version history and lineage for auditability. Set compute quotas on resources and apply policies to ensure adherence to security, privacy, and compliance standards. Build audit trails to meet regulatory requirements as you tag machine learning assets, and automatically track experiments for CI/CD. Use the advanced capabilities to meet governance and control objectives and to promote model transparency and fairness.

Benefit from interoperability with MLflow

Build flexible and more secure end-to-end machine learning workflows using MLflow and Azure Machine Learning. Seamlessly scale your existing workloads from local execution to the intelligent cloud and edge. Store your MLflow experiments, run metrics, parameters, and model artifacts in the centralized Azure Machine Learning workspace.

See machine learning operations in action

Build machine learning pipelines to design, deploy, and manage model workflows

Build machine learning pipelines to design, deploy, and manage model workflows

Build machine learning pipelines to design, deploy, and manage model workflows

Deploy rapidly with confidence using autoscaling and managed, distributed inference clusters

Deploy rapidly with confidence using autoscaling and managed, distributed inference clusters

Deploy rapidly with confidence using autoscaling and managed, distributed inference clusters

Interoperate with Azure DevOps and GitHub Actions to automate machine learning workflows

Interoperate with Azure DevOps and GitHub Actions to automate machine learning workflows

Interoperate with Azure DevOps and GitHub Actions to automate machine learning workflows

Improve governance and cost management across your machine learning projects

Improve governance and cost management across your machine learning projects

Improve governance and cost management across your machine learning projects

See how customers are delivering value with machine learning operations

"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

Additional resources

MLOPs GitHub

See example code

MLOps model management

Read the documentation