AMLS is a 5 ECTS master course, applicable in the catalog 'Knowledge Technologies'. 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 on a microscopic view of internal compilation, execution, and data management techniques.
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.
B: Rewrites and Optimization
C: Execution Strategies
D: Data Storage and Access
E: 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.