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.
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.
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.
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Frequently asked questions
What did this team achieve with this AI workflow?
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 buildin…
What tools did this team use?
Labelbox, Databricks Lakehouse Platform, Amazon S3, CRANE, Databricks.
What results were reported?
Headcount saved vs. building in-house: 10 head count saved; Time savings for generating insights from images: 70%; years of continually decreasing TCO with Databricks: 4 years; Image annotation project duration: months to hours (source-reported, not independently verified).
What failed first in this deployment?
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 s…
How is this marketing ops AI workflow structured?
Image import from S3 → Image annotation for model training → Stakeholder image upload to CRANE → Engagement prediction by channel → Ranked insights delivered → Revenue tracking feedback loop.