Find and fix errors that matter the most
Bid farewell to blindly labeling data and inefficiently improving model performance. Systematically identify, track, and resolve model and label errors.
Evaluate and visualize model performance
Use quantitative metrics like F1 and IoU to evaluate model performance. Visualize performance by comparing model predictions against manually-labeled ground truth.
Track performance across iterations
Perform differential diagnosis between model runs. Track performance at the class level, or on specific slices of data, and focus your next iteration for targeted improvements.
Optimize data labeling budget to boost model performance.
Label the right data, not just more data. Quickly identify the lowest-performing data and use active learning workflows to prioritize the highest-impact data that will lift performance, all from one platform.
Visualize patterns and edge cases
Quickly identify edge cases in your data using model embeddings. Cluster visually similar data to better understand trends in model performance and data distribution.
