AI Glossary

MLOps

MLOps applies DevOps-like practices to machine learning: versioning, testing, deployment, monitoring, and lifecycle management of models.

Definition

MLOps applies DevOps-like practices to machine learning: versioning, testing, deployment, monitoring, and lifecycle management of models.

Plain English explanation

The engineering discipline of keeping ML models reliable from notebook to production.

Technical explanation

MLOps covers experiment tracking, feature stores, model registries, CI/CD for ML, serving, and monitoring for drift and performance.

Why it matters

Models fail in production without lifecycle engineering. MLOps separates notebook demos from operable systems.

Real-world applications

Model deployment pipelines
Monitoring and rollback
Feature consistency between train and serve

Benefits

Improves reliability and auditability
Speeds safe iteration

Limitations

Tool sprawl
Needs organizational process, not only software

Common misconceptions

Buying an MLOps tool does not replace model validation

Model Registry Feature Store

Related industries

Governance Enterprise Software Cybersecurity Healthcare

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