To optimize the fashion experience for 46 million of our customers, Zalando embraces the opportunities provided by machine learning (ML). For example, we use recommender systems so you can easily find your favorite shoes or that great new shirt. We want these items to fit you perfectly, so a different set of algorithms is at work to give you the best size recommendations. Our demand forecasts will ensure that everything is in stock, even when you decide to make a purchase in the middle of a Black Friday shopping spree.
As we grow our business, we look for innovative ideas to improve user experience, become more sustainable, and optimize existing processes. What does it take to develop such an idea into a mature piece of software operating at Zalando's scale? Let's look at it from the point of view of a machine learning practitioner, such as an applied scientist or a software engineer.
Jupyter notebooks are a frequently used tool for creative exploration of data. Zalando provides its ML practitioners with access to a hosted version of JupyterHub, an experimentation platform where they can use Jupyter notebooks, R Studio, and other tools they may need to query available data, visualize results, and validate hypotheses. Internally we call this environment Datalab. It is available via a web browser, comes with web-based shell access and common data science libraries.