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Retiring - Machine Learning Model Management

Microsoft
Manage, deploy and unlock insights from your machine learning models serving in production.

Retiring - Machine Learning Model Management

Microsoft

Manage, deploy and unlock insights from your machine learning models serving in production.

Machine Learning Experimentation will be discontinued January 9th. For the latest in machine learning, use our new service and SDK.

You can use a Machine Learning service workspace to:

  • Run & Monitor Experiments: Submit Experiments for training and automatically track their progress and view logs.
  • Create Pipelines: Machine learning (ML) pipelines are used by data scientists to build, optimize, and manage their machine learning workflows.
  • Register Models: Save scoring logic operations into models to create Docker images and deployments.
  • Build Images: Quickly create Docker images that encapsulate models, scripts and any associated files.
  • Deploy Models: Send scoring requests to web services in Azure Container Instances, Azure Kubernetes Service, or field programmable gate arrays (FPGA).