Practice · Established · Intermediate

MLOps (2)

The discipline of deploying, monitoring and maintaining machine learning systems reliably in production.

What MLOps (2) is

MLOps extends DevOps with the parts that are specific to learned systems: data versioning, experiment tracking, model registries, drift monitoring and retraining pipelines.

How it works

Pipelines automate data validation, training, evaluation gates, registration and deployment. Monitoring covers both service health and prediction quality, with alerts on distribution shift.

Why it matters

A model that is not monitored is a model that is quietly failing. Most of the total cost of an ML system arrives after launch.

Common uses

→Automated retraining
→Model registries and approval workflows
→Drift and performance monitoring

Strengths

✓Reproducibility and auditability
✓Faster safe iteration

Watch for

✓Tooling sprawl
✓Overkill for a single model

Continue exploring

LLMOps (2)Model Evaluation Feature Store AI Governance