A recent but quite common observation in industry is that although there is an overall high adoption of data science, many companies struggle to get it into production. Huge teams of well-payed data scientists often present one fancy model after the other to their managers but their proof of concepts …

As Germany’s biggest online car marketplace, mobile.de provides a personalized web experience. Our Data Team leverages the interactions of our users to infer their preferences. For this tasks we often apply Python and Spark to wrangle massive amounts of data. In this talk, we are going to present …

The Spark data processing platform becomes more and more important for data scientists using Python. PySpark - the official Python API for Spark - makes it easy to get started but managing applications and their dependencies in isolated environments is no easy task.

A common pattern in most data science projects I participated in is that it’s all fun and games until someone wants to put it into production. All of a sudden the crucial question is how to deploy your model, which version, how can updates be rolled out, which requirements are needed and …

Nowadays, Spark surely is one of the most prevalent technologies in the fields of data science and big data. Luckily, even though it is developed in Scala and runs in the Java Virtual Machine (JVM), it comes with Python bindings also known as PySpark, whose API was heavily influenced by …