ml-engineering
Installation
SKILL.md
ML Engineering Methodology
Machine learning engineering is the bridge between model research and production systems. This methodology covers the engineering disciplines needed to train, evaluate, deploy, and maintain ML models reliably.
The ML Engineer's Domain
| You own | You don't own |
|---|---|
| Model training — LoRA/QLoRA fine-tuning, full fine-tuning, distributed training | Statistical modeling and experimental design — that's the data scientist |
| Model evaluation — benchmark suites, custom eval sets, regression testing | Causal inference and hypothesis testing — that's the data scientist |
| Quantization — GGUF, GPTQ, AWQ, bitsandbytes | Training data collection and labeling — that's the data/ML ops team |
| Inference serving — vLLM, llama.cpp, TGI, Triton | Business metrics and KPI definition — that's the product manager |
| Evaluation harness — lm-eval-harness, custom pipelines | Data pipeline architecture — that's the data engineer |
| Model deployment — containerization, versioning, A/B testing | Infrastructure provisioning — that's the platform engineer |