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Machine learning operations (MLOps)

Accelerate automation, collaboration, and reproducibility of machine learning workflows

Streamlined deployment and management of thousands of models across production environments, from on premises to the edge

Fully managed endpoints for batch and real-time predictions to deploy and score models faster

Repeatable pipelines to automate machine learning workflows for continuous integration/continuous delivery (CI/CD)

Continuously monitors model-performance metrics, detects data drift, and triggers retraining to improve model performance

Deliver innovation rapidly

MLOps—machine learning operations, or DevOps for machine learning—is the intersection of people, process, and platform for gaining business value from machine learning. It streamlines development and deployment via monitoring, validation, and governance of machine learning models.

Build machine learning workflows 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 managed online endpoints to deploy models across powerful CPU and GPU machines without managing the underlying infrastructure. 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

Take advantage of built-in interoperability with Azure DevOps and GitHub Actions for seamlessly 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. 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.

Accelerate collaborative MLOps across workspaces

Facilitate cross-workspace collaboration and MLOps with registries. Host machine learning assets in a central location, making them available to all workspaces in your organization. Promote, share, and discover models, environments, components, and datasets across teams. Reuse pipelines and deploy models created by teams in other workspaces while keeping the lineage and traceability intact.

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

Comprehensive security and compliance, built in

Get started with an Azure free account

Start free. Get $200 credit to use within 30 days. While you have your credit, get free amounts of many of our most popular services, plus free amounts of 40+ other services that are always free.

After your credit, move to pay as you go to keep building with the same free services. Pay only if you use more than your free monthly amounts.

After 12 months, you'll keep getting 40+ always-free services—and still pay only for what you use beyond your free monthly amounts.

See how customers are delivering value with machine learning operations

FedEx

"Customers expect timely and accurate information on their packages and a data-based delivery experience. We're helping FedEx stay on the leading edge with Azure Machine Learning, and we're building expertise for future projects."

Bikram Virk, Product Manager, AI and Machine Learning, FedEx
FedEx

BRF

"We're scaling with automated machine learning in Azure and MLOps capabilities in Azure Machine Learning so that our 15 analysts can focus on more strategic tasks instead of the mechanics of merging spreadsheets and running analyses."

Alexandre Biazin, Technology Executive Manager, BRF
BRF

Nestle

"MLOps is at the core of our product. Because of its reproducible ML pipelines, ...registered models, and automatic model scoring, we're definitely detecting things that we missed before. Which, in terms of risk management, is really, really important."

Ignasi Paredes-Oliva, Lead Data Scientist, Nestlé Global Security Operations Center
Nestle Italia

PepsiCo

"We've used the MLOps capabilities in Azure Machine Learning to simplify the whole machine learning process. That allows us to focus more on data science and let Azure Machine Learning take care of end-to-end operationalization."

Michael Cleavinger, Senior Director of Shopper Insights, Data Science, and Advanced Analytics, PepsiCo
PepsiCo

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