In this talk, we will introduce Palladium, an open source framework for easily setting up predictive analytics services (https://github.com/ottogroup/palladium). It supports tasks like fitting, evaluating, storing, distributing, and updating (predictive) models. Core machine learning processes are compatible with the open source machine learning library scikit-learn and thus, a large number of scikit-learn’s features can be used with Palladium. Although being implemented in Python, Palladium provides support for other languages and is shipped with examples how to integrate and expose R and Julia models. For an efficient deployment of services based on Palladium, a script to create Docker images automatically is provided. This talk will cover the use of Palladium including an example where a simple classification service is set up. We will also show how Docker and Mesos / Marathon can be used to deploy and scale Palladium-based services. Having basic knowledge about Machine Learning and/or scikit-learn would be an advantage when attending this talk.