Labelbox Model's recent developments aim to enhance the speed and quality of machine learning model deployment by automating the process of identifying model failures and labeling errors. The platform now offers auto-generated metrics such as precision, recall, and confusion matrices, allowing users to evaluate model performance and make necessary adjustments efficiently. Users can upload model predictions and ground truths to access these metrics, as well as upload custom metrics if needed. The updated features include an interactive NxN confusion matrix and histograms that help pinpoint areas of model underperformance or labeling discrepancies. Additionally, the embedding projector tool aids in error analysis by visualizing data patterns and outliers, supporting up to 50,000 data points for in-depth analysis. The platform's new capabilities in visualizing segmentation masks and the ability to adjust confidence and IOU thresholds further streamline the debugging process, enabling faster identification and resolution of model and data issues.