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Machine learning operations (MLOps)

MLOps is a practice that streamlines the development and deployment of ML models and AI workflows

Streamline the AI app development lifecycle

  • Share and reuse AI models and pipelines in the central repository with Azure Machine Learning registries.
  • Integrate continuous delivery to automate training, prompt tuning, and deployment workflows.
  • Streamline prompt engineering tasks and orchestrate generative AI models with Azure Machine Learning prompt flow.
  • Create scalable, reproducible pipelines with predefined experiments, version control, and data monitoring.
  • Continuously monitor and evaluate model accuracy, data drift, and responsible AI metrics in production.

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

See how customers are innovating with Azure Machine Learning

Try Azure Machine Learning

Access the Azure Machine Learning studio for low-code and no-code project authoring and asset management.
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