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Found 3 Skills
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
Builds production AI/ML systems — model training, fine-tuning, MLOps pipelines, model serving, evaluation frameworks, RAG optimization, and agent orchestration at scale. Use when the user asks to build, train, or deploy ML models, set up MLOps pipelines, optimize RAG systems, create inference endpoints, or design production AI agents.
Use when reviewing ML system design docs, ML/AI project repos, design-doc PRs, RAG/LLM/foundation-model architectures, agentic AI workflows, or production ML readiness. Applies the ML System Design framework by Kravchenko and Babushkin to grade designs, compare docs with code, find critical gaps and low-hanging fruit, and give specific non-cringy praise.