Ludwig AI is an open-source declarative low-code deep learning framework designed to lower the barrier to building custom AI models through simplified configuration.
Key features include declarative configuration via YAML, multimodal data support, configuration validation, distributed training, and model explainability analysis.
Suitable for machine learning researchers, data scientists, developers, and citizen data scientists who need to quickly prototype and deploy AI models.
You define the model by writing a YAML configuration file, then train and evaluate it using Ludwig's Python API or command-line interface.
Supports processing text, images, and tabular data, suitable for classification, regression, sequence generation, and more.
Yes, Ludwig AI includes integration with causal language models from the HuggingFace Hub, for text generation tasks.
Install the core package via pip, and optionally install extras like ludwig[llm], ludwig[distributed] to extend specific features.
It supports distributed training to handle large-scale data and provides optimization features such as automated batch size selection and parameter-efficient fine-tuning (e.g., LoRA).
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