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.
Search based on annotations, metadata, model predictions and more with Nucleus's powerful querying engine.
“KeepTruckin encounters all manners of surprising edge cases in real world data collection, so when it comes to knowing we’re labeling the most valuable subset of our collected data, we turn to Scale Nucleus. Its intuitive visualizations, query engine and Autotag help our teams improve both data quality and models, all in the same motion.”
Engineering Manager AI/Vision Products, KeepTruckin