MLOps

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name: MLOps slug: mlops version: 1.0.0 description: "Deploy ML models to production with pipelines, monitoring, serving, and reproducibility best practices." metadata: {"clawdbot":{"emoji":"🤖","requires":{"bins":[]},"os":["linux","darwin","win32"]}}

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Quick Reference

| Topic | File | Key Trap | |-------|------|----------| | CI/CD and DAGs | pipelines.md | Coupling training/inference deps | | Model serving | serving.md | Cold start with large models | | Drift and alerts | monitoring.md | Only technical metrics | | Versioning | reproducibility.md | Not versioning preprocessing | | GPU infrastructure | gpu.md | GPU request = full device |

Critical Traps

Training-Serving Skew:

Preprocessing in notebook ≠ preprocessing in service → silent bugs

Pandas in notebook → memory leaks in production (use native types)

Feature store values at training time ≠ serving time without proper joins

GPU Memory:

requests.nvidia.com/gpu: 1 reserves ENTIRE GPU, not partial memory

MIG/MPS sharing has real limitations (not plug-and-play)

OOM on GPU kills pod with no useful logs

Model Versioning ≠ Code Versioning:

Model artifacts need separate versioning (MLflow, W&B, DVC)

Training data version + preprocessing version + code version = reproducibility

Rollback requires keeping old model versions deployable

Drift Detection Timing:

Retraining trigger isn't just "drift > threshold" → cost/benefit matters

Delayed ground truth makes concept drift detection lag weeks

Upstream data pipeline changes cause drift without model issues

Scope

This skill ONLY covers:

CI/CD pipelines for models

Model serving and scaling

Monitoring and drift detection

Reproducibility practices

GPU infrastructure patterns

Does NOT cover: ML algorithms, feature engineering, hyperparameter tuning.