Machine Learning Operations (MLOps)

OVERVIEW

Streamline the AI app development lifecycle

Share and reuse AI models and pipelines in the central repository with Azure Machine Learning registries.

Learn more
Integrate continuous delivery to automate training, prompt tuning, and deployment workflows.

Learn more
Streamline prompt engineering tasks and orchestrate generative AI models with Azure Machine Learning prompt flow.

Learn more
Create scalable, reproducible pipelines with predefined experiments, version control, and data monitoring.

Learn more
Continuously monitor and evaluate model accuracy, data drift, and responsible AI metrics in production.

Learn more

BENEFITS

Bring AI into production

Scale and operationalize models for seamless deployment and management.

Quickly build AI workflows

Build pipelines and model workflows to design, deploy, and manage consistent model delivery.

Easily deploy models anywhere

Use managed endpoints to deploy models and workflows across accessible CPU and GPU machines.

Efficiently automate the AI lifecycle

Automate ML and AI workflows using built-in interoperability with Azure DevOps and GitHub Actions.

Achieve governance across assets

Track versions and data lineage. Set quotas and policies for governance, privacy, and compliance.

Centralize tracking

Track run metrics and store artifacts for your experiments using a consistent set of tools with MLflow.

Share assets across teams

Use registries to collaborate across workspaces and centralize AI assets across your organization.

A light blue, green, and yellow gradient abstract background with smooth, blurry edges.

Microsoft was recognized as a Leader in the IDC MarketScape Worldwide Machine Learning Operations (MLOps) Platforms 2022 Vendor Assessment.

Read the blog

Abstract geometric image with sharp angles and a gradient of green and blue hues on a white background.

CUSTOMER STORIES

See how customers are innovating with Azure Machine Learning

A hand holding a smartphone displaying the Sparks app with various offers visible on the screen.

"The solution has increased the robustness of the machine learning system and made our system more aligned with other environments."

Luis Arnedo Martinez, Machine Learning Platform Product Manager, Marks & Spencer

A modern, rectangular building with multiple windows and a landscaped garden in front, featuring flags on tall pole

"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

Two individuals stand outdoors, with one man using a tablet while the other points at the screen.

"Our teams usually test data, get results, and then use it to develop models and algorithms, which we then build into software products. This platform makes the entire process simpler, faster, and more streamlined."

Mogens Mikkelsen, Enterprise Architect, SEGES Innovation

A person wearing climbing gear ascends an indoor rock climbing wall with various colored holds and grips.

"Without Azure AI prompt flow, we would have been forced to invest in quite significant custom engineering to deliver a solution. Instead, we were able to achieve great speed by easily integrating our existing microservices into the prompt flow solution, and easily publishing to our managed endpoints."

Papinder Dosanjh, Head of Data Science & Machine Learning, ASOS