Introducing Ludwig, a Code-Free Deep Learning Toolbox

Over the last decade, deep learning models have proven highly effective at performing a wide variety of machine learning tasks in vision, speech, and language. At Uber we are using these models for a variety of tasks, including customer support, object detection, improving maps, streamlining chat communications, forecasting, and preventing fraud.
Many open source libraries, including TensorFlow, PyTorch, CNTK, MXNET, and Chainer, among others, have implemented the building blocks needed to build such models, allowing for faster and less error-prone development. This, in turn, has propelled the adoption of such models both by the machine learning research community and by industry practitioners, resulting in fast progress in both architecture design and industrial solutions.

At Uber AI, we decided to avoid reinventing the wheel and to develop packages built on top of the strong foundations open source libraries provide. To this end, in 2017 we released Pyro, a deep probabilistic programming language built on PyTorch, and continued to improve it with the help of the open source community. Another major open source AI tool created by Uber is Horovod, a framework hosted by the LF Deep Learning Foundation that allows distributed training of deep learning models over multiple GPUs and several machines.

Extending our commitment to making deep learning more accessible, we are releasing Ludwig, an open source, deep learning toolbox built on top of TensorFlow that allows users to train and test deep learning models without writing code.

Ludwig is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures rather than data wrangling.`

Ludwig

We have been developing Ludwig internally at Uber over the past two years to streamline and simplify the use of deep learning models in applied projects, as they usually require comparisons among different architectures and fast iteration. We have witnessed its value to several of Uber’s own projects, including our Customer Obsession Ticket Assistant (COTA), information extraction from driver licenses, identification of points of interest during conversations between driver-partners and riders, food delivery time prediction, and much more. For this reason we decided to release it as open source, as we believe there is no other solution currently available with the same ease of use and flexibility.

We originally designed Ludwig as a generic tool for simplifying the model development and comparison process when dealing with new applied machine learning problems. In order to do so, we drew inspiration from other machine learning software: from Weka and MLlib, the idea of working directly with raw data and providing a certain number of pre-built models; from Caffe, the declarative nature of the definition file; and from scikit-learn, its simple programmatic API. This mix of influences makes it a pretty different tool from the usual deep learning libraries that provide tensor algebra primitives and few other utilities to code models, while at the same time making it more general than other specialized libraries like PyText, StanfordNLP, AllenNLP, and OpenCV.

Ludwig provides a set of model architectures that can be combined together to create an end-to-end model for a given use case. As an analogy, if deep learning libraries provide the building blocks to make your building, Ludwig provides the buildings to make your city, and you can chose among the available buildings or add your own building to the set of available ones.

The core design principles we baked into the toolbox are:

•     No coding required: no coding skills are required to train a model and use it for obtaining predictions.
•     Generality: a new data type-based approach to deep learning model design that makes the tool usable across many different use cases.
•     Flexibility: experienced users have extensive control over model building and training, while newcomers will find it easy to use.
•     Extensibility: easy to add new model architecture and new feature data types.
•     Understandability: deep learning model internals are often considered black boxes, but we provide standard visualizations to understand their performance and compare their predictions.

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