Toyota Research Institute
Deep learning researchers at Toyota use Weights & Biases to collaborate, streamlining their workflow and creating a living history of their progress.
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Create better machine learning products, faster. Weights & Biases offers everything you need to get your ML products into the hands of your consumers quickly.
Pick the tools that solve your problems best. With W&B, you're not locked in to an end-to-end platform. We pride ourselves on making our tools play well with other systems.
Use this central platform to reliably track all your organization's machine learning models, from experimentation to production. Centrally manage access controls and artifact audit logs, with a complete model history that enables traceable model results.
We've enabled customers to reproduce ML models in minutes vs. days or weeks. Capture all the inputs, transformations, and systems involved in building a production model. Safeguard valuable intellectual property with all the necessary context to understand and build upon models, even after team members leave.
With W&B, your company can stay focused on the hard machine learning problems. Let us take care of tracking all the details to make the models reproducible.
Whether you have a team of 5 or 500, use Weights & Biases to empower your team to share insights, build models faster, and scale with your ever-growing ML initiatives.
Still working with a collection of screenshots, text files, and slide decks to share findings? An ad-hoc system is time-consuming and scattered, and work is not discoverable. With W&B Reports, companies can centralize all of that information, work, and cobbling together of different things into one streamlined process to document work, share insights, collaborate with your team and business stakeholders.
By Alex Paino
How the OpenAI Robotics team uses W&B Reports runs a massive machine learning project.
Data security is a cornerstone of our machine learning platform. We support enterprise installations in private cloud and on-prem clusters, and plug in easily with other enterprise-grade tools in your machine learning workflow.
Everything you log to Weights & Biases is yours, including your training data, code, configuration and hyperparameters, output metrics, analysis, and saved model files. You can choose to log, export, publish, or delete any of these.
How our lightweight, interoperable tools empower your ML team
Version and track every artifact in your model pipeline
Know where a model comes from end to end and what is running where.
Reliably capture all changes to the data you're using to train your models.
Save datasets in any system: GCP, AWS, Azure, or even uploaded directly to W&B servers.
Clearly identify what downstream steps in your pipeline were affected by a change in data.
Capture all of your trained models in one central system.
Maintain a clear picture of all the data, preprocessing, and evaluation that was done on each model.
Trace back the lineage of any production model to the exact code and data that it was trained on.
What data was this production model trained on?
How did relabeling a chunk of data affect the accuracy of this model?
This data was corrupted — what downstream models were affected by this issue?
How did changing this preprocessing step affect model accuracy?
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Every time you run a new experiment, W&B captures the changes you made and gives you a place to jot down notes. Quickly compare your latest results with previous baselines.
Automatically track the exact version of the code, hyperparameters, and even the dataset your model trained on. Trace back exactly where your resulting models came from.
Annotate findings inside the central W&B system so your models can speak for themselves. Use interactive notes, comments, and reports to clearly explain your research.
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What are the key findings from this research?
Deep learning researchers at Toyota use Weights & Biases to collaborate, streamlining their workflow and creating a living history of their progress.
Peter Welinder from OpenAI Robotics describes his process using Weights & Biases to track his team's massive distributed training runs.
Peruse our findings on how building the right technical stack for your machine learning team support core business efforts and safeguards IP.
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