Individual skill
mlops-pro
MLOps guidance — experiment tracking, model registry, feature stores, training pipelines, deployment, and monitoring.
skills/mlops-pro/SKILL.md
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## Overview
MLOps is DevOps for machine learning: the practices that take a model from a notebook to a reliable production system — experiment tracking, reproducible training, model registries, deployment strategies, and monitoring for data/model drift. Most ML projects fail not on modeling but on everything around it: untracked experiments, unreproducible training, and silent degradation in production.
This skill covers the MLOps lifecycle: tracking experiments, versioning data and models, building training pipelines, deploying safely, and monitoring what matters once live.
## When to use
- Setting up experiment tracking (MLflow, Weights & Biases).
- Versioning datasets and models (DVC, model registry).
- Building reproducible training pipelines.
- Deploying models (batch, real-time, edge).
- Monitoring models in production (drift, performance, data quality).
- Choosing MLOps tooling for a team.