Project management and collaboration tools for machine learning projects
Reports let you organize, embed and automate visualizations, describe your findings, share updates with collaborators, and more.
- 1.Collaboration: Share findings with your colleagues.
- 2.Work log: Track what you've tried and plan next steps.
- 3.Automated Visualizations: Integrate model analysis into your model CI/CD pipeline using the Report API.
Capture an important observation, an idea for future work, or a milestone reached in the development of a project. All experiment runs in your report will link to their parameters, metrics, logs, and code, so you can save the full context of your work.
Explain how to get started with a project, share what you've observed so far, and synthesize the latest findings. Your colleagues can make suggestions or discuss details using comments on any panel or at the end of the report.
Include dynamic settings so that your colleagues can explore for themselves, get additional insights, and better plan their next steps. In this example, three types of experiments can be visualized independently, compared, or averaged (SafeLife benchmark experiments →).
Write down your thoughts on experiments, your findings, and any gotchas and next steps as you work through a project, keeping everything organized in one place. This lets you "document" all the important pieces beyond your scripts (example: tuning transformers →).