mlops
v1.0.0Deploy ML models to production with pipelines, monitoring, serving, and reproducibility best practices.
Sourced from ClawHub,
Authored by Iván
Installation
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.