Intermediate · Systems

Model Monitoring

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Continuously tracking the performance and behavior of deployed AI models in production.

Technical Definition

Continuously tracking the performance and behavior of deployed AI models in production.

How it works

Model monitoring is essential for maintaining the effectiveness of AI systems in the real world. It involves observing metrics like prediction accuracy, latency, resource usage, and detecting issues like data drift or concept drift. Timely monitoring helps identify when models need retraining or intervention to prevent performance degradation.

Related Concepts

• Data Drift — When the statistical distribution of inputs to a deployed model changes over time.
• Concept Drift — When the relationship between inputs and the target outcome shifts over time, even if inputs look the same.
• MLOps — A set of practices combining Machine Learning, DevOps, and Data Engineering to reliably and efficiently deploy and maintain ML systems.