Ludwig

🚀 Introducing Ludwig: a declarative deep learning framework that simplifies ML tasks with its data-driven configurations. Build custom models effortlessly, support multi-task learning, and maximize control over model parameters. 🤖💡 #AI #MachineLearning #Ludwig

**Ludwig** is a declarative deep learning framework optimized for efficiency and scale.
**Key features** of Ludwig include building custom models with ease using YAML configuration files, support for multi-task learning, and expert level control over model parameters.
**Ludwig** is designed to be modular, extensible, and engineered for production.
**Installation** of Ludwig is available through PyPi with optional dependencies for full functionality.
**Quick Start** with Ludwig involves following tutorials and examples provided, including large language model fine-tuning.
**Supervised ML** tasks with Ludwig can be achieved by building neural networks for specific applications using CSV datasets.
**Why use Ludwig**: It simplifies machine learning tasks by handling engineering complexities out of the box, offers benchmarking capabilities, and supports multi-modal and multi-task learning.
**Ludwig** allows for highly configurable data preprocessing, modeling, and metric tracking.
**Ludwig** also facilitates easy productionization of deep learning models and integrates with pre-trained models like Huggingface Transformers.
**Tutorials** are available for various tasks such as text classification, image classification, and timeseries forecasting.
**Example use cases** include named entity recognition, sentiment analysis, and chit-chat dialogue modeling.
For **further information**, users can access publications, tutorials, and join the Ludwig community for contributions and updates.