ml-engineering

このSkillについて

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).

利用例

• Planning LoRA or QLoRA fine-tuning runs
• Designing evaluation sets and regression tests
• Comparing quantization and inference deployment options
• Recording model lineage and reproducible training runs
• Triaging model drift and quality regressions

特徴

• References for fine-tuning, evaluation, quantization, inference, and training infrastructure
• Templates for training runs, evaluation regressions, lineage, drift response, and quantization decisions
• Eval manifest covering production ML engineering planning tasks
• Script for detecting training and evaluation corpus overlap
• Guidance on reproducibility, baselines, boundary testing, and test-set leakage

対応Agent

Skillファイル

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