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
Accelerate data curation and mine edge cases by pre-labeling and enriching images and videos using state-of-the-art AI models such as Segment Anything, BLIP, or YOLOv8. Quickly extract insights and achieve remarkable performance across classification, object detection, and segmentation use cases.
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's data engine automates data curation and labeling from 1B+ assets
Blue River Technology needed to rapidly scale and optimize their computer vision model development pipeline and decrease their iteration cycles — which often took several weeks — to hours in order to deliver the best AI-powered products. Two of the primary causes of delay in their processes were data management and infrastructure being created and maintained by ML engineers and an arduous data curation process that took longer and became more painful as the amount of data increased exponentially.
The team built a unified machine learning and data engine that leverages embedded integrations with best-in-class data storage and management, data curation, and labeling solutions. The platform also includes multiple robust and innovative applications designed to increase efficiencies and reduce ML engineering workloads.
With the new data engine, Blue River Technology’s ML teams can now spend more of their time focusing on training, monitoring, and maintaining their computer vision models. Their data scientists can pull updated, refined, relevant datasets for every use case and model within minutes via Labelbox Catalog.
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