Labelbox is the training data platform for modern AI teams.

With Labelbox, you can rapidly create training data with minimal human supervision, train models, manage model experiments and improve model performance within a unified platform. Labelbox is primarily designed to help your AI team build and operate production-grade machine learning systems.

Catalog

Catalog allows you to visualize and search for your raw unlabeled data, metadata, labeled ground truth, and model inferences in one place. No need to waste time and resources building and maintaining infrastructure just to view your data.

You can use Catalog to search for labeled and unlabeled data using filters for metadata, model inferences, and other attributes. Send this batch of data directly to a labeling project in just a few clicks.

Learn more about Catalog.

Annotate

Import data from any source and annotate the data types you need including images, video, text, documents, audio, medical imagery, and tiled imagery/geospatial data.

Learn more about Annotate.

Model training

Turn your labeled data into a trained model without friction. Connect Labelbox to your preferred model training cloud provider or your custom model training service via webhooks or the Python SDK and launch training jobs all from the Labelbox UI. Simplify your pipeline with a single low-code integration.

Learn more about Models.

Model diagnostics

Visualize model errors and take action to improve performance faster. Quickly identify edge cases in your data using model embeddings. Cluster visually similar data to better understand trends in model performance and data distribution. Then, interact with your data visually and compare model predictions to ground truth. Get to know the nuances of your model's performance and add crucial context to metrics like confidence and IoU.

Learn more about Models.

Boost

Boost is a service layer that includes an integrated data labeling service.

  • Workflow set up and ontology defined in just days. Deep experience in project and instruction design to limit mistakes.
  • A team dedicated to driving efficiency and supporting growth while managing cost. Consistent monitoring to minimize risk and maximize quality.
  • Ability to evolve approach as needs shift or workflows gain efficiency. Combine external teams alongside in-house or existing resources.

Learn more about Workforce Boost (data labeling service)


What’s Next
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