# Ludwig — Low-Code Framework for Building Custom AI Models

> Ludwig is a declarative deep learning framework that lets you build, train, and deploy custom AI models without writing code. Define your model architecture in a YAML config, point it at your data, and Ludwig handles preprocessing, training, hyperparameter search, and serving.

## Install

Save in your project root:

# Ludwig — Low-Code Framework for Building Custom AI Models

## Quick Use
```bash
pip install ludwig
ludwig train --dataset your_data.csv --config config.yaml
```
```yaml
# config.yaml
input_features:
  - name: text_column
    type: text
    encoder:
      type: bert
output_features:
  - name: label_column
    type: category
```

## Introduction
Ludwig is a declarative machine learning framework originally developed at Uber and now maintained by the Linux Foundation AI & Data. Instead of writing training loops, you describe your model in a simple YAML configuration file. Ludwig handles data preprocessing, model construction, distributed training, hyperparameter optimization, and model serving automatically.

## What Ludwig Does
- Trains models from YAML configs for tabular, text, image, audio, and multimodal data
- Supports LLM fine-tuning with LoRA and QLoRA via simple configuration changes
- Provides automatic feature preprocessing (tokenization, normalization, encoding) based on data types
- Integrates hyperparameter search via Ray Tune with Bayesian or grid strategies
- Exports trained models for serving via REST API, BentoML, or TorchScript

## Architecture Overview
Ludwig uses an Encoder-Combiner-Decoder (ECD) architecture. Each input feature passes through a type-specific encoder (BERT for text, ResNet for images, numeric embeddings for tabular). A combiner merges encoded representations (concat, transformer, or TabNet). Each output feature uses a decoder head for its task type. The entire pipeline from raw data to predictions is configured declaratively, and Ludwig auto-selects sensible defaults when fields are omitted.

## Self-Hosting & Configuration
- Install via pip: `pip install ludwig` or with LLM support: `pip install ludwig[llm]`
- Define model configs in YAML specifying input features, output features, and optional trainer settings
- Train from CLI: `ludwig train --config config.yaml --dataset data.csv`
- Distributed training with Ray: `ludwig train --backend ray --config config.yaml`
- Serve a trained model: `ludwig serve --model_path results/model` starts a REST API

## Key Features
- Declarative YAML configuration eliminates boilerplate training code
- Multi-modal support: combine text, image, tabular, audio, and time-series inputs in one model
- LLM fine-tuning with LoRA via simple config: set `base_model: meta-llama/Llama-2-7b` and train
- Automatic benchmarking compares multiple model configs and reports metrics
- Built-in visualization for learning curves, confusion matrices, and feature importance

## Comparison with Similar Tools
- **AutoGluon** — AutoML focused on tabular data; Ludwig handles multimodal and LLM fine-tuning with more config control
- **Hugging Face Trainer** — requires Python code; Ludwig uses declarative YAML for the full pipeline
- **Keras** — flexible but imperative; Ludwig is better for rapid prototyping without custom code
- **FLAML** — AutoML library focused on hyperparameter tuning; Ludwig covers the full data-to-serving lifecycle
- **PyCaret** — low-code ML for classical models; Ludwig extends to deep learning and LLM fine-tuning

## FAQ
**Q: Can Ludwig fine-tune large language models?**
A: Yes, Ludwig supports LLM fine-tuning (SFT, LoRA, QLoRA) for models like LLaMA, Mistral, and Falcon via YAML config with minimal setup.

**Q: Do I need to preprocess my data before using Ludwig?**
A: No, Ludwig auto-detects feature types and applies appropriate preprocessing (tokenization, normalization, image resizing) based on the declared type in the config.

**Q: Can Ludwig handle multi-task learning?**
A: Yes, specify multiple output features in the config. Ludwig trains a single model with shared representations and separate decoder heads for each task.

**Q: How does Ludwig compare to writing custom PyTorch?**
A: Ludwig removes boilerplate but is less flexible for novel architectures. It is best for standard tasks where rapid iteration and reproducibility matter more than architectural innovation.

## Sources
- https://github.com/ludwig-ai/ludwig
- https://ludwig.ai/latest

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Source: https://tokrepo.com/en/workflows/d6d1b374-3d9d-11f1-9bc6-00163e2b0d79
Author: AI Open Source