Labelbox has introduced several updates to its suite of tools, including Labelbox Catalog, Annotate, and Model, aimed at enhancing AI teams' capabilities in data labeling and model training. Key features include improved similarity search powered by vector embeddings, which allows users to efficiently identify specific data points within vast datasets, thus refining model performance by addressing edge cases and rare examples. The platform now supports native PDF and text document annotation, utilizing multimodal annotation to extract complex data from documents, which is particularly beneficial in industries like healthcare and finance. To optimize pre-labeling workflows, Labelbox has introduced an automation efficiency score that quantifies the impact of pre-labeling on time and cost savings, enabling teams to better measure and enhance their processes. Additionally, the platform offers tools for curating and versioning hyperparameters and datasets, facilitating model comparison and iteration. These advancements collectively aim to streamline AI development by providing more robust, efficient, and insightful data handling and model training processes.