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“W&B is a key piece of our fast-paced, cutting-edge, large-scale research
workflow: great flexibility, performance, and user experience.”

Adrien Gaidon

Toyota Research Institute

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Weights & Biases is the scalable toolkit for machine learning teams. We're
trusted by over 90,000 machine learning practitioners and over 200 companies,
including OpenAI and Toyota Research Institute.


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SCALABLE AND SECURE

We offer solutions that scale up with massive distributed training, and can be
hosted in our secure hosted cloud or on your own private cloud in a self-hosted
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WITH WEIGHTS & BIASES YOU CAN:

Focus critical developer resources on your core business

Launch new machine learning models faster, with less back and forth

Safeguard IP with a central system of record

Onboard new ML engineers fast, and avoid duplicated work

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A CASE STUDY WITH TRI


OVERVIEW

Toyota Research Institute's mission is to build the safest mobility in the
world. Machine learning teams at TRI are pursuing autonomous driving, and they
use the Weights & Biases system of record to make their models reproducible.

 * Company size: 300+

 * Industry: Autonomous vehicles





PROBLEM

Led by Adrien Gaidon, the ML team built up world-class infrastructure for
training models, but lacked a good way to track and version the valuable
results.

They quickly realized the need for a central system of record, but building a
solution internally was a distraction from the team's core goals.

“It's really hard for machine learning right now to provide any guarantees,
statistical or otherwise, on how reliable it's going to be. Putting in a safety
critical system, it really has to work. How can we make it safe enough so that
we can put it in cars and save lives instead of endanger lives.”

Adrien Gaidon

Toyota Research Institute




SOLUTION

The TRI team compared different solutions for their experiment tracking problem,
and settled on Weights & Biases as the best platform to coordinate machine
learning projects.

Instead of tinkering with brittle internal tools and ad-hoc solutions for
experiment tracking and prediction visualizations, the ML team was able to
standardize with W&B's lightweight experiment tracking and visualization
solutions.

The W&B dashboard gave machine learning practitioners a command center to
compare across dataset and model versions, maintaining a reliable record of
every experiment and result. ML engineers are now free to focus on the valuable
work of model development, accelerating project progress.

“You have to define the metrics clearly when you have a robotic system or a
self-driving car that is extremely hard to test on the public roads for instance
because the safety standards are very high, but at the same time you want
continuous deployment and you want rapid iteration.”

Adrien Gaidon

Toyota Research Institute

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