---
id: irahardianto/awesome-agv/ml-engineering
version: "cd77d70b"
license: MIT
install: manual
updated: 2026-07-17
---

# ml-engineering — Master the principles for constructing reliable machine learning systems from data collection through deployment. Learn pipeline design patterns, feature engineering strategies, model validation checklists, and serving approaches—plus monitoring techniques to detect drift and trigger retraining.

Publisher: irahardianto · Stars: 150 · Updated: 2026-07-17

Install (manual): `git clone https://github.com/irahardianto/awesome-agv`

## SKILL.md

# ML Engineering Principles

Guidelines for building reliable, reproducible machine learning systems.

## When to Invoke
- Designing ML pipelines (training, serving)
- Feature engineering and data preparation
- Model evaluation and validation
- MLOps infrastructure decisions

## ML Pipeline Design

### Stages
```
Data Collection → Feature Engineering → Training → Evaluation → Deployment → Monitoring
```

### Principles
1. **Reproducibility** — versioned data, code, and config. Same inputs = same model.
2. **Experiment tracking** — every run logged (MLflow, W&B, Neptune).
3. **Feature stores** — centralized feature computation, reusable across models.
4. **Model registry** — versioned models with metadata, promotion workflow.

## Feature Engineering

1. **Compute features once, reuse everywhere** — feature store pattern.
2. **Training-serving skew prevention** — same transformation code in training and inference.
3. **Feature documentation** — every feature has description, source, freshness requirement.

## Model Validation

### Checklist
- [ ] Performance metrics meet threshold (accuracy, F1, AUC, etc.)
- [ ] No data leakage (target info in features)
- [ ] Fairness evaluation across protected groups
- [ ] Performance on edge cases and out-of-distribution data
- [ ] Latency meets serving SLA
- [ ] Model size within deployment constraints

## Model Serving

| Pattern | When |
|---|---|
| **Batch inference** | Scheduled predictions, large volumes, latency-tolerant |
| **Real-time API** | Low-latency, per-request predictions |
| **Streaming** | Continuous predictions on event streams |
| **Edge** | On-device, offline-capable |

## Monitoring

1. **Data drift detection** — statistical tests on input distributions.
2. **Model performance monitoring** — track prediction accuracy over time.
3. **Feature importance drift** — alert when feature contributions shift.
4. **Automated retraining triggers** — retrain when performance degrades below threshold.

## Tools Ecosystem

| Category | Tools |
|---|---|
| Experiment tracking | MLflow, Weights & Biases, Neptune |
| Feature stores | Feast, Tecton, Hopsworks |
| Model registry | MLflow, Vertex AI, SageMaker |
| Data versioning | DVC, LakeFS |
| Pipeline orchestration | Kubeflow, Vertex AI Pipelines, Airflow |

## Related
- Data Engineering @.agents/skills/data-engineering/SKILL.md
- Python Idioms @.agents/skills/python-idioms/SKILL.md
- Performance Optimization Principles @.agents/rules/performance-optimization-principles.md


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