Decision Making based on Machine Learning at Outfittery
Outfittery’s mission is to provide relevant fashion to men. In the past it was our stylists that put together the best outfits for our customers. But since about a year ago we started to rely more on intelligent algorithms to augment our human experts. This transition to become a data driven company has left its marks on our IT landscape: In the beginning we just did simple A/B tests. Then we wanted to use more complex logic so we added a generic data enrichment layer.Later we also provided easy configurability to steer processes.And this in turn enabled us to orchestrate our machine learning algorithms as self contained Docker containers within a Kubernetes cluster. All in all it’s a nice setup that we are pretty happy with.
It then really took us some time to realise that we actually had built a delivery platform to deliver just any pure function that our data scientists come up with – directly into our microservices landscape. We just now started to use it that way; we just put their R&D experiments directly into production… 🙂
This talk will guide you through this journey, explain how this platform is built, and what we do with it.