Burberry harnesses Labelbox and Databricks to curate strategic marketing assets
Burberry works with high-volume unstructured image datasets to train object detection and classification models for global marketing campaigns, but manual labeling was not feasible at scale, and the open-source annotation tool they tried lacked proper data-source integration, required local storage, and was too slow.
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 S3
Images are imported to Labelbox from an Amazon S3 bucket via API.
With Labelbox and Databricks, image annotation projects that previously took months now take hours, insight generation dropped from two months to a two-hour self-service process, 10 headcount were saved versus building in-house, time savings for generating insights improved by 70%, and Burberry has achieved four years of continually decreasing total cost of ownership.
What failed first
Burberry first tried an open-source image annotation tool that had serious drawbacks: it lacked integration with their data sources, required images to be stored locally on a data scientist's machine, and could only save progress in JSON files.