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.”
Toyota Research Institute