Monitoring

Intermediate

Observing model inputs/outputs, latency, cost, and quality over time to catch regressions and drift.

Full Definition

Observing model inputs/outputs, latency, cost, and quality over time to catch regressions and drift.

Keywords

drift performance

Domains

MLOps & Infrastructure

Related Terms

Training Pipeline related to

End-to-end process for model training.

 Inference Pipeline related to

Model execution path in production.

 Batch Inference related to

Running predictions on large datasets periodically.

 Online Inference related to

Low-latency prediction per request.

 Domain Shift related to

A mismatch between training and deployment data distributions that can degrade model performance.

 MLOps related to

Practices for operationalizing ML: versioning, CI/CD, monitoring, retraining, and reliable production management.

 CI/CD for ML related to

Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.

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