Customers
& case studies‍

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

"We now can understand corner cases better by sorting hundreds of thousands of samples — and exploring their corresponding metrics, ground-truth, and model-prediction visualizations — all via W&B tables"

“Weights & Biases gave our team a full and complete understanding of our model’s lineage, from datasets to training to production artifacts. We saw a 15% increase in our model building efficiency while saving about 15% on hardware spend on top of that.”

“[With Weights & Biases] we demonstrated our workflow in training high-capacity models, reducing overfitting while increasing model capacity, and maintaining fast iteration speed.”

“We’re now driving 50 to 100 times more experiments [with W&B] versus what we were doing before on the Mk1 IPU systems.”

“W&B lets us examine all of our candidate models at once. We can identify which model produces state of the art results on our robust test suite. This is vital for understanding which model will work best for each customer.”

“Building a good process for deploying and monitoring the models [is really important to us]. Managing experiments and showing the report and seeing everything really helps us to get understanding on how it’s exactly done.”

“We got to the point where we had so many models and data versions that we simply couldn’t manually keep track of all of them. Once we started taking advantage of Artifacts, it’s been very helpful."

“We were drawn to W&B because we realized our existing approach just didn’t work with a remote team. W&B is a much better home for our experimentation results. Plus it’s super easy to use. ”

“With Weights & Biases, we just create this hyperparameter sweep. You don’t have to change anything in your code. So it just save you a lot of headaches if you can run this automatically without much thinking. ”

"To monitor and evaluate our machine learning runs, we have found the Weights & Biases platform to be the best solution. Their API makes it fast to integrate W&B logging into an existing codebase."

“We ended up using Weights & Biases a lot for tracking our experiments. I think that brought us a certain level of sanity in all the chaos that was from all of the research that we were doing as the team grew.”

"You just add some log calls and you’re there,” said Ganesh Yalla, Data Science Lead at Capella Space. “It was very refreshing for me."

“[W&B] has been a great solution for logging and tracking runs. It made it easy for people to debug, check in on each other’s work, and have more insight into what’s happening.”

“Saving everything in your model pipelines is essential for serious machine learning: debugging, provenance, reproducibility. W&B is a great tool for getting this done.”

“The last mile of deploying machine learning to production is really long. So having a team and tools [like W&B] dedicated to focusing on just how hard that last mile is has really paid off.”

"Once people started seeing the value of W&B, it kind of just exploded, and everyone on the ML team now builds everything on it in the company."

"Now when we train, we don’t talk to S3. We don’t need to know details about where it’s stored. We just talk to the Artifacts registry and we can track the lineage for everything."