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

1. Identify problem type (binary/multi-class classification, regression, etc.)
2. Define evaluation metrics (accuracy, F1, RMSE, etc.)