Case Study: Wayve scales end-to-end MLOps with Weights & Biases

Wayve Implements End-to-End MLOps with W&B

Wayve, a London-based autonomous mobility AI company, needed robust MLOps infrastructure to help its ML engineers move efficiently from experimentation to production. To support its end-to-end ML lifecycle, Wayve partnered with Weights & Biases (W&B) and used its platform to track experiments, compare runs, monitor training, and improve data exploration and developer productivity.

With Weights & Biases, Wayve automatically logged hyperparameters, metrics, and artifacts in one place, while also using W&B to monitor GPU, CPU, networking, and IO utilization and create Reports to share experiment context across teams. The result was better insight into training, improved use of compute resources, and a significant boost in throughput, with the number of experiments run in parallel growing exponentially.

Wayve

Peter Matev

Engineering Manager