concept#Machine Learning#Observability#Data#Reliability

Model Monitoring

Continuous monitoring of machine learning models in production to detect performance degradation, drift, and faulty predictions.

Model monitoring refers to the continuous observation of machine learning models in production to detect performance degradation, data and concept drift, and faulty predictions early.

Maturity

Emerging

Cognitive loadMedium

360° overview

Model Monitoring

Six perspectives place the building block in context. The numbers show where each perspective continues in the reading path.

The building block at a glance

Model Monitoring

360°

Classification

2

Complexity: Medium
Impact area: Technical

Technical context

2

Integrations

Feature store / event streaming (e.g. Kafka)

+2

Principles & goals

1

Continuously monitor model‑relevant data and performance metrics.

Value stream stage: Run

Use cases & scenarios

4

Use cases: 3
Scenarios: 3

Compromises

3

Risks: 3
Best practices: 3

I/O & resources

4

Inputs: 3
Outputs: 3