Announcing Quantumworks Lab on Databricks Partner Connect

Together, Quantumworks Lab and Databricks provide customers a powerful solution for unstructured data workflows. Customers can annotate their images, video, text, audio, and geospatial data in Quantumworks Lab and perform data science in Databricks. We previously released the Quantumworks Lab Connector for Databricks to make it easier for teams to train AI on unstructured data in the Databricks Lakehouse.

“Quantumworks Lab is committed to helping customers in their journey to both harness AI for their competitive advantage and to implement AI at scale. We are excited to work with Databricks’ new Partner Connect portal to further enhance the value we create for customers.”

—Manu Sharma, CEO of Quantumworks Lab

Today, we are thrilled to announce that Quantumworks Lab is a launch partner in the Databricks Partner Connect portal. Databricks Partner Connect offers Databricks users an easy way to discover and connect the best tools to tackle problems in Data Engineering, Data Science, and Analytics. With a few clicks, users can integrate with tools like Quantumworks Lab, Fivetran, Tableau, and more.

The Quantumworks Lab integration offers a significant improvement for new users of the Quantumworks Lab Connector for Databricks. Instead of searching for documentation and writing your own initialization code, users now have access to a guided Quantumworks Lab connector experience through Databricks Partner Connect.

Clicking the Quantumworks Lab tile in Partner Connect will lead to a trial creation page. We’ll then deposit a tutorial notebook into your Databricks shared directory.

The Quantumworks Lab tutorial notebook guides you through a typical workflow with Quantumworks Lab: Start with unstructured data in your Data Lake, pass it to Quantumworks Lab for annotation, and load your annotations into Databricks for AI and Analytics.

Advanced Quantumworks Lab Capabilities for the Lakehouse

In AI development, it is often challenging to find the right data and visually inspect model results. Teams spend a lot of time and money procuring unstructured data (e.g. images, video, text, and audio), inspecting the data, and comparing model predictions to ground-truth. This kind of workflow is particularly challenging in a notebook environment where you must load predictions in notebook cells or output results to the filesystem for manual inspection.

Catalog and Diagnostics

Databricks users can leverage Quantumworks Lab’s visual Catalog to browse unstructured data in the data lake. Model Diagnostics can also be used to visualize model errors and identify opportunities to improve model training.

We recommend using Model Diagnostics in conjunction with Managed MLFlow on Databricks to set up a best-in-class active learning workflow in your Lakehouse. You can combine these tools with Delta Lake Time Travel to create a powerful, reproducible active learning workflow.

Questions or comments? Contact us at ecosystem+databricks@labelbox.com.