Customers & case studies

Trusted by 800,000 users and 1000+ companies from the most cutting-edge and innovative AI startups and research institutions to the biggest brands around the world.
Weights & Biases is the tool of choice for machine learning practitioners. See how some of the most innovative ML teams in the world depend on Weights & Biases to track, compare, and visualize their ML experiments to build better models, faster.

The world’s leading AI teams trust Weights & Biases

Customer stories

How Microsoft Leveraged Weights & Biases to Build the Models Behind Ink

“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. ”

Lyft’s High-Capacity End-to-End Camera-Lidar Fusion for 3D Detection

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

Toyota Research Institute Tracks Experiments using Weights & Biases

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

Scaling IPU Experimentation to Support Next Generation of Large Models with the Help of Weights & Biases

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

AI for AG: Production Machine Learning for Agriculture

“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.”

How Woven Leverages W&B to Drive Continuous Learning

“Experiment tracking has given us 10x velocity and enabled us to share results with each other much faster, with tractability and traceability”

Making Simulations More Human with Inverted AI

“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.”

How Socure Fights Fraud with Machine Learning

“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.”

Leveraging AI for Visual FX at MARZ

“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.”

Designing ML Models for Millions of Consumer Robots

“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.”

How Capella Space Produces World Class Satellite Data with W&B

“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.”

OpenAI’s Robotics Team Visualizing Model Training

“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.”

How Pandora Deploys ML Models into Production

“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. ”

Deployment and Monitoring for Smart Baby Monitors at Nanit

“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.”

Democratizing AI for Biology with OpenFold

“[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.”

Square Brings Conversational AI to Businesses of All Sizes With W&B

“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.”

How Cohere Trains Business-Critical LLMs with the Help of W&B

“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.”

How Weights & Biases Helps Salesforce with the Challenges of Making ML Work in the Real World

“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.”

Kabam Keeps Game Development Front and Center with W&B

“Before W&B, whenever there was a behavior discrepancy, it was really hard to figure out what was wrong. Now, whenever we have a problem, we can usually solve it quickly, and most of the time, it’s as simple as looking at our dashboards.”

How Personal AI Utilizes W&B to Empower Individuals With Personal Language Models

“I’ve worked with and trained models for over 15 years, and I have never encountered a platform as simple and intuitive as W&B. It’s a beautiful solution to maximize model performance while minimizing developer overhead.”

Northwestern Medicine Delivers Timely and Quality Care with W&B

“As an anesthesiologist, I often take care of patients undergoing cancer surgery—not a month goes by where I don’t have the privilege of helping take care of someone who had an abnormal finding flagged by our tool.”

Devsisters Levels Up Game Development with W&B

“W&B helped us a lot in model development and model updates. Without the visualization of our metrics, it’s very hard to know where the problems are and see what we should change.”

VisualCortex Takes Video Analytics to Enterprise with W&B

“The reason we can have such a small team is because W&B helps us coordinate everything. The platform removes a lot of the manual work for us and helps us automate processes so each member can focus on their core role and not worry about the mechanics and governance.”

Carbon Re Develops Cutting Edge Decarbonization Technology with W&B

“We use the model registry as our source of truth. It’s a key component of our backend.”

M-KOPA Transforms Their Model Management Workflow with W&B

“Building trust internally is always a hard thing for data scientists. A lot of the things we do are quite obscure, so trust has to be built through concrete demonstration. Having a system like Weights & Biases in place, designing processes to answer their questions, really helps build that trust.”

Aleph Alpha Builds Europe’s Most Advanced LLMs with W&B

“W&B gives us a concise look at all projects. We can compare runs, aggregate them all in one place, and intuitively decide what works well and what to try next.”

How Graphcore Is Supporting the Next Generation of Large Models With the Help of W&B

“We’re now driving 50 or 100 times more experiments versus what we were doing before on the Mk1 IPU systems”

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

“We actively use W&B to track all our experiments, and it’s allowed us to dig deep into comparisons and monitor in real-time exactly what’s happening in training.”

ECMWF Accelerates Machine Learning Research in Weather Forecasting With W&B

“W&B has been helpful for us to try loads of different approaches and understand which ideas and what changes led to improvements.”

Open Climate Fix Improves Solar Forecasting With W&B

“We’re still a small team working on developing our workflows, and W&B has been a big help in optimizing and automating a lot of those key processes.”

Designing Streamlined Model Management Workflows at Canva Using W&B

“The W&B Model Registry simplifies our lives in so many ways. It brings less noise to the user experience, as we are now only seeing models that are production-ready. It stores all the production-level information we need.”

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