Recent advances in deep learning have enabled research and industry to master many challenges in computer vision and natural language processing that were out of reach until just a few years ago. Yet computer vision and natural language processing represent only the tip of the iceberg of what is possible. In this article, I will demonstrate how Sebastian Heinz,
Roland Vollgraf and I (Calvin Seward) used deep neural networks in steering operations at Zalando’s fashion warehouses.
As Europe’s leading online fashion retailer, there are many exciting opportunities to apply the latest results from data science, statistics, and high-performance computing. Zalando’s vertically integrated business model means that I have dealt with projects as diverse as computer vision, fraud detection, recommender systems and, of course, warehouse management.
To solve the warehouse management problem that I’ll discuss in this post, we trained a neural network that very accurately estimates the length of the shortest possible route that visits a set of locations in the warehouse. I’ll demonstrate how we used this neural network to greatly accelerate a processing bottleneck, which in turn enabled us to more efficiently split work between workers.
The core idea is to use deep learning to create a fast, efficient estimator for a slow and complex algorithm. This is an idea that can (and will) be applied to problems in many areas of industry and research. Continue reading