building-automl-pipelines

6.4

155

109

Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning. Use when automating ML workflows from data preparation through model deployment. Trigger with phrases like "build automl pipeline", "automate ml workflow", or "create automated training pipeline".

automl

6.4

Rating

Installs

Machine Learning

Quick Review

Well-structured AutoML pipeline skill with clear prerequisites, step-by-step instructions, and proper separation of concerns through referenced files. The description adequately conveys when and how to invoke the skill. Task knowledge appears comprehensive with implementation details, error handling, and examples delegated to reference files. Structure is clean with a concise overview and modular organization. Novelty is moderate - while AutoML pipelines involve complexity, many frameworks (TPOT, PyCaret, H2O) already abstract much of this work, though coordinating the full workflow (validation, deployment artifacts, evaluation reports) adds value beyond simple library usage.