Data Science, Delivered Continuously
AutoScout24 is the largest online car marketplace Europe-wide for new and used cars. With more than 2.4 million listings across Europe, AutoScout24 has access to large amounts of data about historic and current market prices and wants to use this data to empower its users to make informed decisions about selling and buying cars. We created a live price estimation service for used vehicles based on a Random Forest prediction model that is continuously delivered to the end user.
Predictive analytics of such sort is often only used for guiding company internal decision making. Delivering a predictive analytics product straight to the end user poses an entirely different set of requirements with respect to (1) performance and (2) automated quality control.
In order to avoid the effort of handcrafting a high-performance implementation of a complex prediction model, many companies fall back to use primitive prediction models in such a situation. Learn how we achieved superb performance and scalability without the need for manual optimization or sacrifices in terms of prediction accuracy.
For quality control, Continuous Delivery is already an established approach to modern web application development that allows for much shorter product release cycles and therefore yields the ability to rapidly innovate and adapt to user needs. However, in predictive analytics Continuous Delivery has been rarely applied so far. Learn how automated verification using live test data sets in a continuous delivery pipeline allows us to release model improvements with confidence at any given time. This way our users can benefit immediately from the work of our data scientists.
Prerequisite attendee experience level: advanced