Architecture of Machine Learning Systems (2022)

Description:
This course covers the architecture and essential concepts of modern ML systems for both local and 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 is composed of an introductory keynote, lectures, as well as a series of exercises. All basic concepts are augmented with pointers to recent research directions.

The course is conceptually divided into two parts:

A: Overview and ML System Internals covering topics, such as architectures, languages, operators, and various optimization techniques (runtime adaptation, parallel execution, HW accelerators, caching, data organization, etc.). 

B: ML Lifecycle Systems covering topics, such as data acquisition, cleaning, preparation, model selection, model debugging, fairness, explainability, etc.

Prerequisites:
- A general background in computer science
- Basic courses in data management or databases at undergraduate level
- Basic courses on applied ML or data mining at undergraduate level

Organizer: Katja Hose

Lecturers: Matthias Boehm (TU Graz)

ECTS: 2.0

Time: 29-30 August 2022

Place: Room 02.13 both daysTBA

Number of seats: 30

Deadline: 8 August 2022


Important information concerning PhD courses:
We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.