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Last month, Databricks announced the creation of Databricks Ventures, a strategic investment vehicle to foster the next generation of innovation and technology harnessing the power of data and AI. We launched with the Lakehouse Fund, inspired by the growing adoption of the lakehouse architecture, which will support early and growth-stage companies extending the lakehouse ecosystem or powered by lakehouse. That’s why today, I’m thrilled to share Databricks Ventures’ first announced investment: Labelbox.

Labelbox is a leading training data platform for machine learning applications. Rather than requiring companies to build their own expensive and incomplete homegrown tools, Labelbox created a collaborative training data platform that acts as a command center for data scientists to collaborate with dispersed annotation teams.

Together, Databricks and Labelbox deliver an ideal environment for unstructured data workflows. Users can simply take unstructured data (images, video, text, geospatial and more) from their data lake, annotate it with Labelbox, and then perform data science in Databricks.

Earlier this year, Labelbox launched a connector to Databricks so customers can use the Labelbox training data platform to quickly produce structured data from unstructured data, and train AI on unstructured data in the Databricks Lakehouse. Labelbox is also a launch partner for Databricks Partner Connect, which offers customers an even easier way to configure and integrate Databricks with Labelbox. We have been impressed by the Labelbox team and the company’s momentum since we first started working with them. Investing in Labelbox is a natural next step and solidifies our shared commitment to delivering streamlined, powerful capabilities for joint customers to manage unstructured data workflows. Databricks Ventures is excited to support Labelbox and our rapidly growing number of joint customers even more closely in the future.

Check out the Labelbox connector for Databricks.

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Why We Invested in Labelbox: Streamline Unstructured Data Workflows in a Lakehouse

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