Data Labeling Platform

Label Studio Enterprise

Unlock the power of data-centric AI with the data labeling platform trusted by more than 250,000 data scientists and annotators.

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Identify, catalog, and operationalize all your unstructured datasets in a single view.

Source and select the best data for your training or fine-tuning task.

Enable human feedback to give your models a competitive edge.

Integrate your machine learning backend with Label Studio to:

Pre-label samples based on model predictions to expedite and simplify manual labeling.
Autolabel samples based on model predictions.
Continuously feed your model new labels for online learning.
Perform active learning by creating labeling tasks as requested by your model.
Or, simply connect your data to begin labeling.

Organize your human workforce with projects, roles, customizable workflows, and collaboration tools.

Create workspaces and projects to manage team collaboration.
Define roles and access at the project level for team members and outside contractors.
Define rules and criteria to automatically assign or manually assign tasks to annotators.
Configure annotator workflows and permissions at a granular level to follow your labeling process and guidelines.

Easily customize the labeling interface to fit your use case and data type using a variety of pre-made templates or simple HTML.

Improve data quality and model performance with powerful reviewer workflows and dashboards.

Speed up the labeling process, increase annotation quality, and build more solid labeling and review processes with comments and notifications.

Comment on tasks to raise issues with subject matter experts and reviewers; see notifications in your workspace to respond.
Submit annotation drafts with comments before submitting final annotations that affect project statistics.
Sort tasks in the data manager by unresolved comments, the total number of comments, or specific comment authors.

Ensure your training and fine-tuning data delivers optimal model results with analytics and configurable reviewer workflows.

Identify and resolve problematic items quickly using the smart annotator agreement matrix.
Configure reviewer workflows by model confidence score to enable human-in-the-loop review of annotations that a machine learning model was less certain about.
Review tasks by annotator to assess individual performance, or by agreement score to prioritize annotations with the most uncertainty among annotators.

Monitor progress, throughput, and labeling effectiveness with performance dashboards.

Make informed decisions quickly to improve efficiency and effectiveness of data labeling projects.
View project-level highlights for lead times and other KPIs by user-defined time periods.
Drill into time series charts for specific work being performed for tasks, annotations, and reviews.
View label distribution for the top 30 labels in a project.

We never touch your data, and our platform is SOC2 certified.

SOC2 certified. Leverage our cloud infrastructure while meeting compliance standards.
Audit logs. Keep track of user activity.
Secure architecture. Load data into the interface without it ever touching our servers.

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