Lehrinhalte
This module covers the architecture and essential concepts of modern machine learning (ML) systems for both local and large-scale machine learning. These architectures include systems for data-parallel execution, parameter servers, ML lifecycle systems, and the integration of ML into database systems. The covered topics focus both on a microscopic view of internal compilation, execution, and data management techniques, as well as a macroscopic view of end-to-end ML pipelines. In detail, the module covers the following topics which also reflect the lecture calendar (with a separate 90-120min lecture per topic):
A: Overview and ML System Internals
01 Introduction and Overview
02 Languages, Architectures, and System Landscape
03 Size Inference, Rewrites, and Operator Selection
04 Operator Fusion and Runtime Adaptation
05 Data- and Task-Parallel Execution
06 Parameter Servers
07 Hybrid Execution and HW Accelerators
08 Caching, Partitioning, Indexing and Compression
B: ML Lifecycle Systems
09 Data Acquisition, Cleaning, and Preparation
10 Model Selection and Management
11 Model Debugging, Fairness, and Explainability
12 Model Serving Systems and Techniques
The module contains both lectures and exercises/programming projects which are offered as separate courses in order to increase flexibility.
