Bid farewell to blindly labeling data and inefficiently improving model performance. Systematically identify, track, and resolve model and label errors.
Use quantitative metrics like F1 and IoU to evaluate model performance. Visualize performance by comparing model predictions against manually-labeled ground truth.
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.
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.
Quickly identify edge cases in your data using model embeddings. Cluster visually similar data to better understand trends in model performance and data distribution.