Catalog eliminates the need to build your own traditional and vector database infrastructure. Build better AI applications by leveraging out-of-the-box search for images, text, videos, conversations, and documents across metadata, vector embeddings, and annotations.Learn more
Not all data impacts model performance equally. Through our active learning workflows and uncertainty sampling, you can filter for data with low-confidence predictions to curate and label the right data–not just more data.Learn more
Use inbuilt natural language search, pre-computed, or custom vector embeddings to find similar data clusters for automated classification. Optionally send to human review for maximal accuracy.Learn more
View a detailed class distribution of ground truth labels or model inferences to get a better understanding of your data. See how performance metrics like F1 score vary across your data so you can make the most informed decisions when curating data to label.
Not all data impacts model performance equally. Leverage your data distribution, model predictions, model confidence scores, and similarity search to curate high-impact unlabeled data that will boost your model performance.
Don’t let searching for data and edge cases slow your team down or hold up conversations with stakeholders or customers. Instead of relying on one-off query scripts, search and discover data faster inside Catalog.
How Blue River Technology used model-assisted labeling to reduce labeling costs by 50%
High labeling costs associated with the costs of crops vs. weeds on full image segmentation images.
Labelbox’s model-assisted labeling and collaborative annotation suite.
The company is now able to standardize how they create and manage data all in a single location and using Labelbox's automation suite, they’ve seen a reduction in labeling times by 50%, worth millions of dollars in cost savings per year.
How Burberry harnesses Labelbox and Databricks to curate their strategic marketing assets
Retail and ecommerce
How Ancestry prioritizes collaboration and training data quality to enable genealogical breakthroughs with ML
Technology and software