Ludwig

Ludwig is an open-source, no-code machine learning toolbox built in Python that enables users to develop, train, evaluate, and deploy deep learning models with minimal coding. It provides a declarative, configuration-driven approach to machine learning, allowing users to work with structured data, text, images, audio, time series, and other data types. Ludwig automates data preprocessing, feature engineering, model training, hyperparameter optimization, and evaluation, making it suitable for both beginners and experienced machine learning practitioners.

Features of Ludwig:

• No-code, configuration-based workflow for building machine learning and deep learning models.
• Supports multiple data types, including tabular data, text, images, audio, sequences, and time series.
• Automatically performs data preprocessing, feature encoding, model training, and evaluation.
• Built on modern deep learning frameworks with optional GPU acceleration for faster model training.
• Supports hyperparameter optimization, experiment tracking, and model explainability.
• Provides built-in support for distributed training, inference, and model export for production deployment.
• Extensible architecture that allows users to customize encoders, decoders, trainers, and model configurations.
• Open-source under the Apache License 2.0 with active community development and comprehensive documentation.

To verify the working of Ludwig in your shell:

$ sudo su
$ cd /opt/ludwig
$ source venv/bin/activate
$ ludwig version

# If the command above is unavailable:
$ python -m ludwig version

Disclaimer: Ludwig is an open-source machine learning framework released under the Apache License 2.0. It is maintained by the Ludwig open-source community. Users are responsible for validating models, datasets, and configurations before deploying them in production environments. Always consult the official Ludwig documentation for the latest installation instructions, supported features, and best practices.