Individual skill
building-automl-pipelines
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".
skills/.curated/building-automl-pipelines/SKILL.md
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- ai Declared skill tag
- deployment Declared skill tag
- ml Declared skill tag
- devops Derived category
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SKILL.mdcore · 2,830 bytesassets/README.mddoc · 366 bytesassets/evaluation_report_template.htmlasset · 5,091 bytesassets/example_dataset.csvasset · 1,119 bytesassets/pipeline_template.yamlasset · 3,667 bytesreferences/README.mddoc · 66 bytesreferences/errors.mddoc · 1,167 bytesreferences/examples.mddoc · 443 bytesreferences/implementation.mddoc · 1,902 bytesscripts/README.mddoc · 490 bytesscripts/pipeline_deployment.pyruns · 5,570 bytes
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# Building Automl Pipelines
## Overview
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).
## Prerequisites
Before using this skill, ensure you have:
- Python environment with AutoML libraries (Auto-sklearn, TPOT, H2O AutoML, or PyCaret)
- Training dataset in accessible format (CSV, Parquet, or database)
- Understanding of problem type (classification, regression, time-series)
- Sufficient computational resources for automated search
- Knowledge of evaluation metrics appropriate for task