A growing ecosystem of integrations

Burberry uses thousands of images from multiple sources for its global marketing campaigns. The company must classify these valuable assets precisely so that it can use them effectively in its campaigns to drive the right action by the right audience. Burberry works with a high-volume of unstructured data sets to train their object detection and classification models. They quickly realized it wasn’t possible to identify and label all these images manually and needed their image recognition models to identify highly specific products and apparel SKUs.

Burberry implemented Labelbox’s data engine within its Databricks Lakehouse Platform environment. Image annotation projects that used to take months now take just hours, and marketing team members now have access to powerful content insights without asking for help from data scientists. Databricks’ Lakehouse Platform continues to give Burberry a total lower cost of ownership than the company’s original business case projected.

1. 10 head count saved by implementing Labelbox rather than building in-house.

2. 70% improvement in time savings for generating insights from images.

3. 4 years of continually decreasing total cost of ownership (TCO) with Databricks.