LF AI & Data Foundation—the organization building an ecosystem to sustain open source innovation in artificial intelligence (AI), machine learning (ML), deep learning (DL), and Data open source projects, today is announcing Ludwig as its latest Incubation Project.
Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is your data, a list of fields to use as inputs, and a list of fields to use as outputs, Ludwig will do the rest. Simple command line interfaces and programmatic APIs can be used to train models both locally and in a distributed way, and to use them to predict on new data. Ludwig was released and open sourced by Uber.
“We are very pleased to welcome Ludwig to LF AI. AI, ML, and DL can be perceived as a difficult technology to use. Ludwig provides the opportunity for less experienced engineers and data scientists to use DL models in their work, providing easy-to-use tools and API’s.” said Dr. Ibrahim Haddad, Executive Director of LF AI & Data. “We look forward to supporting this project and helping it to thrive under a neutral, vendor-free, and open governance.” LF AI & Data supports projects via a wide range of benefits; and the first step is joining as an Incubation Project.
Dr. Piero Molino, Ludwig’s creator and maintainer, said: “I’m excited about Ludwig joining the Linux Foundation. The open governance will allow for both increased participation from the community and companies already using it as well as opening the door to new collaborations. This is definitely a step towards Ludwig’s goal of democratizing AI, ML and DL.”
LF AI & Data will support the neutral open governance for Ludwig to help foster the growth of the project. Key features for Ludwig include:
- General: A new data type-based approach to deep learning model design that makes the tool suited for many different applications.
- Flexible: Experienced users have deep control over model building and training, while newcomers will find it easy to use.
- Extensible: Easy to add new model architecture and new feature data-types.
- Understandable: Deep learning models internals are often considered black boxes, but we provide standard visualizations to understand their performances and compare their predictions.
- Easy: No coding skills are required to train a model and use it for obtaining predictions.
- Open: Ludwig is released under the open source Apache License 2.0.
Ludwig’s type based abstraction allows to define combinations of inputs and output types to create deep learning models to solve many different tasks without writing code: a text classifier can be trained by specifying text as input and category as output, an image captioning system can be trained by specifying image as input and text as output, a speaker verification model can be obtained providing two audio inputs and a binary output, and a time series forecasting can be obtained by providing a time series as input and a numerical value as output. By combining different data types, the number of tasks are limitless.
Despite not requiring any coding skills, Ludwig also provides an extremely simple programmatic interface, that allows for training deep learning models and uses them for prediction in just a couple lines of code. It also comes with already built in REST serving capabilities, visualizations of models and predictions, and extensible interfaces to add your own models and hyperparameter optimization.
Check out the Getting Started guide to to start working with Ludwig today. Learn more about Ludwig on their website and be sure to join the Ludwig-Announce and Ludwig-Technical-Discuss mail lists to join the community and stay connected on the latest updates.
A warm welcome to Ludwig and we look forward to the project’s continued growth and success as part of the LF AI & Data Foundation. To learn about how to host an open source project with us, visit the LF AI & Data website.
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