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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
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 deployment.
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
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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