Agent skill · magnus919
ml-engineering
Plan and execute production ML engineering work — model training and fine-tuning (LoRA/QLoRA), evaluation and eval-set design, quantization decisions, inference deployment, 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).
What it needs
About 3k tokens when loaded.
What this skill does
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, 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 res …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills magnus919/ml-engineering