Labelbox expands partnership with Google Cloud to help teams build AI applications faster

Enterprises are now racing to build foundational AI capabilities and leverage the growing amounts of data available to them. The ability to leverage that data easily and quickly for developing intelligent applications is becoming essential to staying competitive. However, curating and preparing high quality data for use in machine learning training for building AI-enabled applications can be extremely time and resource intensive.

To help enterprises accelerate their data and AI journeys, Labelbox, an AI platform for building intelligent applications, and Google Cloud, today announced an expanded, multi-year partnership to support digital transformations in key industries, including retail, consumer internet, financial services, and manufacturing and logistics.

ML teams can now leverage Labelbox’s AI platform and Google Cloud’s leading AI and Data Cloud tools, solutions, and services, including BigQuery and Vertex AI. These integrations can be used to shorten the development cycles for generative AI applications by providing easy to use data labeling capabilities that is essential to preparing often proprietary data that is required for iterative fine tuning enterprise applications.

“By uniting these platforms, we’re allowing teams to get a faster, simpler, and more complete solution for building and scaling next-generation AI applications,” says Manu Sharma, CEO & Co-founder of Labelbox

Labelbox’s integration with Vertex AI will reduce the time required to label unstructured image, text, audio, and video data, which helps accelerate model development for image classification, object detection, entity recognition, and various other tasks. With the integration only available on Google Cloud, Labelbox and Vertex AI create a flywheel for accelerated data curation and model development. Data scientists can now leverage the power of unstructured data to build more specialized and effective ML models using Vertex AI after enriching their training data in Labelbox.

Together, BigQuery and Labelbox also deliver an ideal environment for unstructured data workflows. With the Labelbox Connector for BigQuery, users can deploy a visual layer on top of their data warehouse, allowing teams to perform weak supervision, similarity search and bulk labeling tasks. These workflows allow data teams to draw deeper business insights within BigQuery and as they engage in multiple data science and machine learning activities across the ML lifecycle.

“Labelbox provides important tooling and capabilities for AI and ML developers to better manage and utilize vast amounts of data. By integrating the Labelbox platform with BigQuery, developers working on the next generation of AI applications get an easy to use workflow to add labelling to their data warehouse ,” said Manvinder Singh, Managing Director, Partnerships at Google Cloud.

For more details, you can learn more about the partnership here. Labelbox on Google Cloud is generally available today and can be deployed via the Google Cloud Marketplace. Teams can get started and access the Labelbox Connector for BigQuery here.