Selecting the right data for training machine learning models is a significant challenge for AI teams, and public datasets offer a valuable starting point, albeit with difficulties in browsing and finding specific data. Labelbox addresses these challenges by enabling users to browse over 30 large-scale public datasets through its Catalog, allowing visualization, organization, and analysis of extensive datasets without the need for technical expertise or downloading large volumes of data. Through Labelbox, teams can explore datasets like the LAION Aesthetics, which traditionally required significant technical proficiency to access. The platform offers features such as natural language search and similarity search to easily discover relevant data, helping users assess dataset quality, identify bias, and examine duplicates. These functionalities support more efficient data curation and selection, crucial for optimizing ML workflows, and invite AI teams to enrich their models with innovative public datasets.