Nucleus helps teams build better datasets. Bring your data, labels, and model predictions together to debug your ML models and improve your datasets.
See insights, search on custom metadata, and create data slices to track model performance on specific scenarios.
Sort predictions by error metrics or explore interactive confusion matrices to quickly find specific examples of each model failure.
Curate your unlabeled data with active learning to prioritize the highest-value data to send for labels next.
Send data to Scale in one click for labeling, label it yourself with Nucleus’s built-in editor, or import and export labels via API.
Share links to slices, queries, and individual examples. Have ML engineers, labelers, and data ops specialists collaborate on the same platform.
Automate dataset uploads, add metadata, upload model predictions, and export from Nucleus with an intuitive API.
Optimize your labeling spend by identifying class imbalance, errors, and edge cases in your data with Scale Nucleus.
Scale is trusted by leading machine learning teams to develop more accurate models.