Ludwig

Overview

Ludwig is a low-code framework for building custom LLMs, neural networks, and other AI models. It provides a declarative approach to model definition, training, and inference using YAML configuration files and a Python interface.

Best for

Best for
Developers and data scientists who want to quickly experiment with and deploy custom AI models without deep coding

Use cases

• Rapidly prototype and train custom text, image, or tabular models without writing extensive code
• Fine-tune large language models for domain-specific tasks using a configuration-driven workflow
• Build and compare multiple model architectures through simple YAML configuration changes

Notes

Ludwig is a low-code framework for building custom LLMs, neural networks, and other AI models. It provides a declarative approach to model definition, training, and inference using YAML configuration files and a Python interface.

11,707 stars on GitHub. Last updated 2026-05-29. Licensed Apache-2.0.

Use cases

• Rapidly prototype and train custom text, image, or tabular models without writing extensive code
• Fine-tune large language models for domain-specific tasks using a configuration-driven workflow
• Build and compare multiple model architectures through simple YAML configuration changes

Pros

• Reduces boilerplate and accelerates model development for practitioners
• Supports a wide variety of data types and model architectures out of the box
• Active open-source community with over 11k stars on GitHub

Cons

• Performance and flexibility may lag behind fully custom PyTorch or TensorFlow implementations
• Debugging complex custom behaviors can be challenging due to abstraction layers
• Documentation and examples may not cover all edge cases or advanced use cases

Indexed from awesome-llmops and enriched against its public facts.