LabelStudio + Galileo: Fix your ML data quality 10x faster

The use of machine learning in everyday applications has raised concerns about the quality of training data, which is crucial for high-performing models. Data scientists spend a significant amount of time on tasks such as data selection, labeling, and evaluation to ensure high-quality outputs. To speed up the ML workflow, it's essential to choose the right data-centric tools, particularly for data labeling and inspection. Galileo automates data inspection by identifying low-quality data, reducing labeling costs by over 40% and improving model performance by over 20%. When used with Label Studio, a popular open-source labeling platform, Galileo can significantly improve the efficiency of building ML models, detecting annotation mistakes, and monitoring production data to train with next.