Fix Data Errors with Galileo and Label Studio

Overview

Galileo is a machine learning data quality platform data scientists use to find and correct errors and biases in their ML data. Annotation errors or “mislabels” can occasionally be found in ML datasets — impacting the overarching quality of the dataset. Galileo offers seamless integration with Label Studio to empower users to automatically detect and re-label annotation mistakes in their data or add more context to improve the model.

Galileo uses advanced algorithms to detect data drift, automatically marking key data to be annotated with the Label Studio integration. With a single button click, a user can identify unlabeled data belonging to a new cluster, create a Label Studio project from within the platform, and send the data to Label Studio along with instructions to the labeler.

Benefits

• Accelerate Labeling Workflows: The Galileo integration with Label Studio creates efficiencies in the data labeling process through a single-click integration which helps generate correctly labeled, clean ML datasets in hours rather than weeks.
• Continuously Improve Model Performance: Use Galileo to debug models, identify and fix labeling errors within a single workflow to increase model accuracy (and the annotator’s efficiency).
• Semantic Data Drift Detection: Using an advanced algorithm, Galileo detects new clusters of ML data the model receives from production, AKA "drifted data.”
• Further Context Behind Annotations: Combined with Label Studio, Galileo provides further context behind annotations, with enhanced instructions provided to the annotator.

Together, Galileo and Label Studio create a powerful team for more accurate and efficient data labeling processes across teams.