Computer Vision Pipeline with Kubernetes


We produce a multitude of attributes (characteristics attached to an entity -- building, parcel, etc.) using various sources such as aerial imagery. The idea is to build Deep Learning models from a few thousand buildings using in-house-tagged labels or existing labels from open data. In a second step, the models are deployed on the whole French territory, which represents more than 35 million images to process (i.e. 4 TB of data to deal with). This second step is the focus of this post. The challenge is to be able to infer at low cost and in a short amount of time, (less than a day).

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