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Model Monitoring

Model Monitoring

[ˈmɒdl ˈmɒnɪtərɪŋ]
AI Infrastructure
Last updated: December 9, 2024

Definition

The continuous tracking of deployed AI models to ensure they maintain performance and reliability in production.

Detailed Explanation

A systematic approach to tracking model performance, data drift, prediction quality, and system health in production environments. Includes monitoring of metrics, alerts, logging, and automated responses to degradation in model performance.

Use Cases

Financial trading systems, Customer recommendation engines, Production ML pipelines

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