ml-engineering

name	ml-engineering
description	Machine learning engineering methodology — model training, fine-tuning (LoRA/QLoRA), evaluation, quantization, deployment, and MLOps pipeline design. Grounded in practical engineering patterns for production ML systems.
version	1.1.0
author	Hermes Agent community
license	MIT
metadata	{"hermes":{"tags":["ml","machine-learning","fine-tuning","training","evaluation","quantization","mlops","inference","vllm","gguf"],"related_skills":["data-scientist","platform-engineering","site-reliability-engineering"]}}

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

Reference Files

Reference	When to load
references/fine-tuning.md	Setting up a LoRA/QLoRA/ full fine-tuning run — data prep, hyperparameters, validation strategy
references/evaluation.md	Evaluating a model — benchmark selection, custom eval sets, regression tracking, comparison methodology
references/quantization-inference.md	Quantizing a model and serving it — GGUF/GPTQ/AWQ/bitsandbytes comparison, calibration data strategies, KV cache quantization, vLLM/llama.cpp/TGI/Triton architecture, production considerations
references/training-infrastructure.md	Selecting and provisioning training infrastructure — GPU selection, multi-GPU training, cloud vs on-prem

Core Principles

Measure before you optimize — Never quantize, prune, or distill a model without first measuring its baseline performance. Optimization without measurement is guessing.

Reproducibility is non-negotiable — Every training run needs a reproducible config: seed, data version, hyperparameters, and evaluation methodology. If you can't reproduce it, you can't ship it.

Baseline first — Before running an expensive fine-tuning run, establish a baseline with the base model. If the base model is already good enough, the fine-tuning budget is better spent elsewhere.

Test at the boundary — Model evaluation is most informative at the edges of the capability distribution, not at the center. Hard examples reveal more than easy ones.

The evaluation set is a liability — Every example in your eval set is a potential test-set leak. Use held-out sets, rotate examples, and periodically audit for contamination.