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