Organizations are increasingly adopting AI and machine learning to maximize the value of their unstructured data, with a focus on a data-centric approach that emphasizes data quality, diversity, and accessibility. Databricks, an analytics platform built on Apache Spark, supports this approach by providing a collaborative environment and leveraging Delta Lake for scalable and reliable data storage and processing. By integrating Databricks with Labelbox, companies can streamline the transformation of unstructured data into model-ready training data, utilizing tools like Catalog and Annotate for data visualization, enrichment, and curation. This integration is enhanced by foundation models, such as GPT-4, and features like the auto-segment tool from Meta’s Segment Anything Model, facilitating the rapid development of production-ready ML models. The Labelbox Connector for Databricks simplifies the automation of data ingestion and annotation processes, enabling efficient data management and reducing the time needed to prepare high-quality training data.