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Lifecycle management

Machine learning models in production environments have a complex lifecycle, and the use and value of models requires a robust and repeatable process to manage that lifecycle. Without proper management, models that reach production may deliver inaccurate data, poor performance, or unexpected results that can damage your business’s reputation for AI trustworthiness. Lifecycle management is essential for creating a machine learning operations system that allows you to scale many models in production.

The following sections describe how to manage models in production. Be sure to review the deployment considerations before proceeding.

Topic Describes...
Deployment inventory (Deployments page) Coordinate deployments and view deployment inventory.
Manage deployments Understand the actions you can take with deployments.
Deployment settings Configure and view deployment settings.
Enable accuracy monitoring Set an association ID and add actuals in order to enable accuracy monitoring.
Replace deployed models Replace the model used for a deployment.
Set up Automated Retraining Configure retraining policies to maintain model performance after deploying.

Updated March 1, 2023
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