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

• Target variable and feature columns clearly defined

Instructions

• Identify problem type (binary/multi-class classification, regression, etc.)

• Define evaluation metrics (accuracy, F1, RMSE, etc.)

• Set time and resource budgets for AutoML search

• Specify feature types and preprocessing needs

• Determine model interpretability requirements

• Load training data using Read tool

• Perform initial data quality assessment

• Configure train/validation/test split strategy

• Define feature engineering transformations

• Set up data validation checks

• Initialize AutoML pipeline with configuration

See ${CLAUDE_SKILL_DIR}/references/implementation.md for detailed implementation guide.

Output

• Complete Python implementation of AutoML pipeline

• Data loading and preprocessing functions

• Feature engineering transformations

• Model training and evaluation logic

• Hyperparameter search configuration

• Best model architecture and hyperparameters

Error Handling

See ${CLAUDE_SKILL_DIR}/references/errors.md for comprehensive error handling.

Examples

See ${CLAUDE_SKILL_DIR}/references/examples.md for detailed examples.

Resources

• Auto-sklearn: Automated scikit-learn pipeline construction with metalearning

• TPOT: Genetic programming for pipeline optimization

• H2O AutoML: Scalable AutoML with ensemble methods

• PyCaret: Low-code ML library with automated workflows

• Automated feature selection techniques