automl-optimizer

What problem does it solve?

Automates the process of hyperparameter tuning and model selection by exploring model and hyperparameter spaces with AutoML libraries, tracking experiments, and delivering optimal configurations with minimal manual intervention.

Core Features & Use Cases

• Automated Hyperparameter Optimization: leverages Optuna, Hyperopt, and AutoML libraries to search broad spaces efficiently.
• Strategy-based AutoML: supports hyperparameter optimization, algorithm selection, and neural architecture search with end-to-end experiment tracking.
• Practical Integration: logs experiments to .specweave increments, supports living docs and reproducibility, and provides guidance for common ML workflows.

Quick Start

Run the AutoML optimizer with an increment to start a hyperparameter search and review the best model.