Burberry harnesses Labelbox and Databricks to curate strategic marketing assets
Burberry uses thousands of images from multiple sources for global marketing campaigns and must classify them precisely to drive the right action by the right audience. Training its object detection and classification models required labeling massive volumes of images — a task that was not feasible to do manually at the company's scale.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Image import from Amazon S3
Marketing images are imported to Labelbox from an Amazon S3 bucket via API.
Image annotation projects that previously took months now take hours, and insight generation was reduced from two months to a two-hour self-service process. Burberry saved 10 headcount compared to building in-house, achieved a 70% improvement in time savings for generating insights from images, and has seen 4 years of decreasing total cost of ownership with Databricks.
What failed first
Burberry first tried an open source image annotation tool that lacked an interface to read from their data sources, required storing all images locally on a data scientist's machine, and could only save progress in a JSON file — making it impractical at the company's scale.