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Machine learning models in production environments have a complex lifecycle; maintaining the predictive value of these 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.

Topic Describes how to
Challengers Compare model performance post-deployment.
Retraining Define the retraining settings and then create retraining policies.
Humility Monitor deployments to recognize, in real-time, when the deployed model makes uncertain predictions or receives data it has not seen before.

Updated April 3, 2024