Modulhandbuch (Module manual)

​Profound knowledge in the field of machine learning (e.g. course „Data Science for Business“) and advanced programming skills (Python)​.

​In research and academia, we often build machine learning (ML) models without embedding them in real-world applications or deploying them in production. Designing productive ML systems is both complex and unique. ML systems are complex because they consist of many components (e.g., big data, deep learning algorithms, high-performance computing infrastructure, interactive user interfaces) and involve many stakeholders (e.g., data scientists, managers, users). ML systems are unique because they are custom-built to solve a specific task (e.g., predict the probability that a customer will churn) and depend on proprietary data (e.g., behavioral data and transaction histories of a company’s customers). In this project-based module, students will learn how to develop, deploy, operate, monitor, and improve ML systems that solve real-world problems. As part of the projects, they will collaborate with external stakeholders (i.e., companies and research projects) to understand their business problems, gather and analyze requirements, and collect feedback.​