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name: ml-engineering description: >- Plan and execute production ML engineering work — model training and fine-tuning (LoRA/QLoRA), evaluation and eval-set design, quantization decisions, inference deployment, lineage, feature parity, drift response, and regression triage, grounded in practical engineering patterns for production ML systems. Do not use for statistical modeling and experimental design (that's the data scientist) or for operating a specific inference engine (that's a tool skill such as llama-cpp or vllm). license: MIT metadata: tags: ml, machine-learning, fine-tuning, training, evaluation, quantization, mlops, inference, vllm, gguf source_repo: https://github.com/magnus919/hermes-profiles

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/evaluation-and-lineage.md	Metric/configuration decisions, repeated stochastic comparisons, end-to-end lineage, temporal feature parity, drift response, and adaptation/serving tradeoffs
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, VRAM budgeting, multi-GPU strategies (DDP/FSDP/DeepSpeed), cloud vs on-prem, storage, monitoring

Templates

Template	When to Use
templates/training-run-record.md	Recording a training or fine-tuning run — model and data versions, full config, environment, eval results — so it can be reproduced
templates/eval-regression-table.md	Tracking model quality across runs and triaging a regression — one row per eval case or capability subset
templates/quantization-decision-record.md	Recording a quantization decision — baseline, candidates compared, quality threshold, and rollback path
templates/model-lineage-record.md	Linking data/features, code/configuration, runs, artifacts, evaluations, registry state, and deployed serving versions
templates/drift-response-record.md	Recording drift signals, thresholds, diagnosis, retrain/rollback decisions, and post-action evidence

Scripts

Script	When to Use
scripts/check-eval-overlap.py	Checking a training corpus against an eval corpus for test-set leakage (shared n-grams); --json for CI, exit 1 when an eval file exceeds the overlap threshold

Evals

evals/evals.json — output-quality eval manifest for this skill: fine-tuning plan review, eval-set design, quantization decision, deployment plan, regression triage, and training-run reproducibility.

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 with the overlap checker.

When not to use

Do not use this skill for statistical modeling, experimental design, or causal inference — that's the data scientist's discipline. Do not use it to operate a specific inference engine: for llama.cpp installation, model loading, benchmarking, and troubleshooting, load the llama-cpp tool skill instead; for vLLM deployment, model configuration, benchmarking, batching tuning, GPU operation, and upgrade/rollback, load the vllm tool skill instead. This skill provides the methodology (eval-set design, quantization trade-offs, deployment plans, regression triage); the tool skills own the runbooks.