Not being able to manage intellectual property generated during AI development (Model Research and Development Team)

In AI development, the intellectual property is the learning results, learning data, and learning know-how, rather than the code. By registering assets in AI development and managing their relationships in a traceable manner, you can maximize the reproducibility, quality, and effectiveness of AI development.

There are barriers to team collaboration (Model Research and Development Team)

AI development is becoming increasingly large-scale. Whether or not you can quickly scale individual developer insights to a team will determine success or failure in this highly competitive field. Remove the barriers to collaboration within a team and build a development system that is not overly dependent on individuals.

Low observability makes it difficult to improve application quality (Application Development Team)

Even if you can see the input and output of the developed application, you cannot see the internal process. If it becomes a black Box, it becomes difficult to accurately evaluate it and improve its quality with confidence.

Track and visualize all your ML experiments

In the image segmentation example, you can interactively monitor loss and IoU improvements in real time, and automatically track system metrics such as GPU and CPU usage.

It automatically captures information about your experiments, and the multimedia-enabled table function makes it possible to extract insights from your results that can lead to improvements.

Share your insights with live reports

You can create and share reports with the same interactive features as the W&B Dashboard. You can also add comments, so you can keep a log of the discussions that took place during development.

Organize all the information generated by your generative AI workflow, from experimentation to evaluation to production

All input and output data is automatically recorded, allowing you to define your own evaluation methods and measure performance under different scenarios. Models and datasets can also be saved and versioned within the platform, allowing you to improve the quality of your applications.