Machine Learning as a microservice in a Docker container on a Kubernetes cluster — say what?

It is always fascinating to see the versatile ways in which machine learning can be used. At Outfittery, algorithms help the experts select the most suitable outfits for customers — quite literally. In an interview at W-JAX 2017 in Munich, Jesper Richter-Reichhelm, CTO at Outfittery GmbH, explains how the company uses machine learning and which frameworks they use. He also tells us who makes better suggestions — human beings or machines.

JAXenter: At Outfittery, you don’t only use outfit experts, but also machine learning. How exactly is this possible? Did you first test what the algorithm and the human expert would suggest —separately— or did you have the algorithm solve some issues and the expert other problems?

Jesper Richter-Reichhelm: We apply data science in two different fields: on the one hand, for the optimization of business processes, e. g. in the allocation of the purchasing budget. On the other hand, we use machine learning to support stylists in their decision-making processes, for example by recommending clothing sizes.

The latter is about providing the human experts with assistance resulting from the analysis of the data. This increases the efficiency of the goods we send to the customer, especially the quality of the content of the box that we send to the customer.

JAXenter: What were the first steps from the technical side?

Jesper Richter-Reichhelm: We have been using machine learning in the company for several years. For almost two years now, however, we have taken the integration of machine learning algorithms into the IT platform to a new level by implementing what we call “smart gateway”.

From a technical point of view, we can call up a service that makes a decision: a so-called decision point. For example, for an A-B test, this can be the simple decision as to which test group a customer is in. However, there can also be an arbitrarily complex algorithm behind it, which analyzes the data of a customer and creates an instruction for action, e. g. not to send an article with the wrong size, style or price to a customer. From a technical point of view, the caller is only a REST call from one microservice to another in both cases.

In the beginning, this Smart Gateway was still very simple, rather a prototype. In the meantime, the service has undergone various evolutionary steps and forms the standard for the integration of machine learning algorithms into the production system.