Create repeatable AutoML training pipelines that check data, preprocess features, search and tune models, evaluate results, and export deployment artifacts. Set evaluation metrics and time or compute budgets to guide automated model selection.
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".
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See how to installReview the instructions the agent will receive before you install.
Build an end-to-end AutoML pipeline: data checks, feature preprocessing, model search/tuning, evaluation, and exportable deployment artifacts. Use this when you want repeatable training runs with a clear budget (time/compute) and a structured output (configs, reports, and a runnable pipeline).
Before using this skill, ensure you have:
See references/implementation.md for detailed implementation guide.
See references/errors.md for comprehensive error handling.
See references/examples.md for detailed examples.
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