No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox

No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox

  • June 28, 2019
Table of Contents

No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox

Uber AI’s Piero Molino discusses Ludwig’s origin story, common use cases, and how others can get started with this powerful deep learning framework built on top of TensorFlow. Machine learning models perform a diversity of tasks at Uber, from improving our maps to streamlining chat communications and even preventing fraud. In addition to serving a variety of use cases, it is important that we make machine learning as accessible as possible for experts and non-experts alike so it can improve areas across our business.

In this spirit, we built Ludwig, an open source, deep learning toolbox built on top of TensorFlow that allows users to train and test machine learning models without writing code. To explain how this powerful framework works, Piero Molino, Ludwig creator and Uber AI senior research scientist, discusses Ludwig’s origin story, common use cases, and how others can get started with the software: Interested in learning more about Ludwig and AI at Uber? Check out the Ludwig repo and keep up-to-date with other projects Uber AI by subscribing to the Uber Engineering Newsletter!

Special thanks to Piero Molino, Wayne Cunningham, Stan Yee, Seamus Strahan-Malik, Deidre Locklear, Robert Brent Wilson, Blake Henderson, and Doug Rae for their contributions to this video.

Source: uber.com

Tags :
Share :
comments powered by Disqus

Related Posts

How AI is Starting to Influence Wireless Communications

How AI is Starting to Influence Wireless Communications

Machine learning and deep learning technologies are promising an end-to-end optimization of wireless networks while they commoditize PHY and signal-processing designs and help overcome RF complexities What happens when artificial intelligence (AI) technology arrives on wireless channels? For a start, AI promises to address the design complexity of radio frequency (RF) systems by employing powerful machine learning algorithms and significantly improving RF parameters such as channel bandwidth, antenna sensitivity and spectrum monitoring. So far, engineering efforts have been made for smartening individual components in wireless networks via technologies like cognitive radio.

Read More
Releasing Pythia for vision and language multimodal AI models

Releasing Pythia for vision and language multimodal AI models

Pythia is a deep learning framework that supports multitasking in the vision and language domain. Built on our open-source PyTorch framework, the modular, plug-and-play design enables researchers to quickly build, reproduce, and benchmark AI models. Pythia is designed for vision and language tasks, such as answering questions related to visual data and automatically generating image captions.

Read More
DeepMind and Google: the battle to control artificial intelligence

DeepMind and Google: the battle to control artificial intelligence

One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: “So today I’m going to be talking about different approaches to building…” He stalled, as though just realising that he was stating his momentous ambition out loud.

Read More