What is Ludwig?
Ludwig is an open-source declarative machine learning framework that enables users to train and deploy ML models without writing code. Built by Uber, Ludwig uses a simple YAML configuration file to define your machine learning pipeline, making it accessible to domain experts without extensive ML engineering knowledge.
Declarative
Configure models with YAML instead of code
Multi-Modal
Handle text, images, audio, and tabular data
Production-Ready
Built-in serving and deployment capabilities
Ludwig Performance Calculator
Basic Ludwig Configuration
Ludwig uses declarative YAML configuration files to define machine learning pipelines. Here's a simple example for tabular data classification:
model_type: ecd
input_features:
- name: text_feature
type: text
preprocessing:
tokenizer: space_punct
- name: numerical_feature
type: number
preprocessing:
normalization: zscore
- name: categorical_feature
type: category
output_features:
- name: target_label
type: category
trainer:
epochs: 100
batch_size: 128
optimizer:
type: adam
learning_rate: 0.001
preprocessing:
split:
type: random
probabilities: [0.7, 0.1, 0.2]Train your model with a single command:
# Train the model
ludwig train --config config.yaml --dataset data.csv
# Generate predictions
ludwig predict --model_path results/model --dataset test.csv
# Serve the model via REST API
ludwig serve --model_path results/modelReal-World Examples
Uber - Origin Development
Uber developed Ludwig to democratize machine learning across their organization.
- • 50+ ML models built by non-ML engineers
- • 10x faster time-to-prototype for new ML applications
- • 90% reduction in ML pipeline code
Financial Services - Fraud Detection
A major bank uses Ludwig for real-time fraud detection across multiple channels.
- • 15M+ transactions processed daily
- • 99.8% accuracy on fraud classification
- • 2 week model development cycle (down from 6 months)
Healthcare - Medical Image Analysis
Research hospital uses Ludwig for automated medical image classification.
- • 500K+ medical images in training dataset
- • 96% accuracy on radiological findings
- • 80% faster diagnosis workflow
Advanced Features
Multi-Modal Learning
input_features:
- name: product_image
type: image
preprocessing:
resize_method: interpolate
height: 224
width: 224
- name: product_description
type: text
preprocessing:
tokenizer: bert
- name: price
type: number
output_features:
- name: category
type: categoryHyperparameter Optimization
hyperopt:
goal: maximize
metric: accuracy
split: validation
parameters:
trainer.learning_rate:
space: loguniform
lower: 0.0001
upper: 0.1
trainer.batch_size:
space: choice
categories: [16, 32, 64, 128]Best Practices
✅ Do
- •Start with simple configurations and iterate
- •Use proper data preprocessing and validation splits
- •Leverage built-in visualizations for model analysis
- •Monitor training with tensorboard integration
- •Use hyperparameter optimization for better performance
❌ Don't
- •Skip data preprocessing and quality checks
- •Use default settings without understanding them
- •Ignore data imbalance in classification tasks
- •Train without proper validation strategies
- •Deploy models without proper testing
Production Deployment
REST API Serving
from ludwig.api import LudwigModel
import pandas as pd
# Load trained model
model = LudwigModel.load("path/to/model")
# Batch prediction
predictions = model.predict(pd.read_csv("new_data.csv"))
# Single prediction
single_prediction = model.predict({
"text_feature": "example text",
"numerical_feature": 42,
"categorical_feature": "category_a"
})Docker Deployment
FROM ludwigai/ludwig:latest
COPY model/ /opt/ludwig/model/
COPY config.yaml /opt/ludwig/
EXPOSE 8080
CMD ["ludwig", "serve", "--model_path", "/opt/ludwig/model", "--host", "0.0.0.0", "--port", "8080"]