Can I optimize for multiple metrics simultaneously?

Yes, the skill supports multi-objective optimization, allowing you to find Pareto-optimal solutions for competing goals like accuracy versus inference speed.

Which optimization frameworks does this skill support?

The AutoML Optimizer integrates with industry-standard libraries including Optuna, Hyperopt, Auto-sklearn, and H2O AutoML.

Does it work with deep learning models?

Yes, it includes specialized strategies for Neural Architecture Search (NAS) to optimize layers, activation functions, and dropout rates in deep learning frameworks.

How does this skill handle experiment tracking?

It automatically logs every trial, including parameters and metrics, to the .specweave directory, creating a persistent and shareable history of your optimization process.

AutoML Optimizer

Name: AutoML Optimizer
Author: anton-abyzov

byanton-abyzov

•

Data Science & ML

Automates hyperparameter tuning and model selection using industry-standard frameworks like Optuna and Auto-sklearn.

About

The AutoML Optimizer skill for SpecWeave streamlines the machine learning development lifecycle by automating the exhaustive trial-and-error process of model tuning. It systematically explores hyperparameter spaces, performs neural architecture searches, and compares multiple algorithms to identify the most efficient and accurate configurations. By integrating directly with the SpecWeave framework, it automatically logs every experiment, generates detailed optimization reports, and updates project documentation, ensuring that model selection remains data-driven, reproducible, and production-ready with minimal manual intervention.

Key Features

Automated algorithm selection and performance comparison across multiple ML models
Multi-objective optimization to balance metrics like accuracy, latency, and training speed
Systematic hyperparameter optimization using Bayesian techniques via Optuna and Hyperopt
Neural Architecture Search (NAS) for optimizing deep learning structures
Automatic experiment tracking and living documentation within the SpecWeave framework
13 GitHub stars

Use Cases

Fine-tuning gradient boosting models like XGBoost or LightGBM for maximum predictive accuracy
Generating comprehensive performance reports and parameter importance charts for ML audits
Finding the optimal architecture for deep learning models without manual experimentation

About

Key Features

Automated algorithm selection and performance comparison across multiple ML models
Multi-objective optimization to balance metrics like accuracy, latency, and training speed
Systematic hyperparameter optimization using Bayesian techniques via Optuna and Hyperopt
Neural Architecture Search (NAS) for optimizing deep learning structures
Automatic experiment tracking and living documentation within the SpecWeave framework
13 GitHub stars

Use Cases

Fine-tuning gradient boosting models like XGBoost or LightGBM for maximum predictive accuracy
Generating comprehensive performance reports and parameter importance charts for ML audits
Finding the optimal architecture for deep learning models without manual experimentation