Increase efficiency of manual data labeling with automated workflows and team performance management.
Ensure accuracy of ground truth datasets with reviewer workflows and quality reporting.
Use one platform for all data types and formats with templates and SDKs to easily configure labeling tasks.
Leveraging prompt-based evaluation, real-time scoring, user feedback, and incremental training, you can implement a feedback loop to iteratively validate and improve the LLM's performance.
Objectively measure accuracy, precision, recall, and more to understand how well your model is performing.
Validate a model’s ability to make accurate predictions on unseen data, identifying any overfitting or underfitting issues. Trust that your model can generalize its learnings effectively.
Validate multiple models against the same ground truth dataset to determine which one performs the best.
Scoutbee has seen significant success with their ML-driven products while using Label Studio Enterprise to both train large-scale models and provide adjustments to their models currently in production.
Try the platform used by more than 250,000 data scientists and experts. Make your labeling team more efficient with workflows, analytics, annotator management tools. Simplify your labeling efforts by using the same platform to label any data type. And integrate any model, including foundation models like GPT-4 to automate your labeling and maximize the impact of the human signal your labelers provide.Explore the Platform