AMLS is a 5 ECTS master course, applicable in the catalog 'Knowledge Technologies' as well as the upcoming catalogs 'Data Science', 'Machine Learning', and 'Interactive and Visual Information Systems'. This course covers the architecture and essential concepts of modern ML systems for supporting large-scale machine learning (ML). These architectures include systems for data-parallel execution (e.g., Spark, Mahout, SystemML), Parameter Servers (e.g., TensorFlow, MXNet, PyTorch), ML lifecycle systems, and the integration of ML into database systems. The covered topics focus primarily on a microscopic view of internal compilation, execution, and data management techniques, but also include a macroscopic view of entire ML pipelines.
In detail, the course covers the following topics, which also reflects the course calendar. All slides will be made available prior to the individual lectures.
A: Overview and ML System Internals
B: ML Lifecycle Systems
The lectures are accompanied by a mandatory open source programming project for gaining practical experience (at the extend of 2 ECTS, i.e, roughly 50 working hours). We'll make project topic suggestions in the context of SystemDS (an open source ML system for the end-to-end data science lifecycle), but your own project proposals (potentially in other open source systems) are welcome as well.