Description:
The course will cover central concepts, methods, techniques, and tools within
DataOps and MLOps. Topics include Data Augmentation, Labeling, Cleaning,
Pre-processing, Quantifying the Data Quality, Lifecycle, machine learning model
deployment, ML pipeline orchestration, monitoring and maintenance (via updating with transfer learning OR
retraining) in production, ensemble algorithms, and technical infrastructure.
Prerequisites:
Bachelor and master degrees in computer science or software engineering, including knowledge on machine learning and data management as introduced in typical undergraduate courses, as well as significant practical experience with these topics.Organizer: Torben Bach Pedersen
Lecturer: Professor Alexandros Nanopoulos, University of Hildesheim
ECTS: 2.0
Time: September 5-7, 2022
Place: Physical, at Department of Computer Science, AAU, Selma Lagerløfs Vej 300, 9220 Aalborg Ø, room 0.2.90
Number of seats: 15
Deadline: August 15, 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.
- Teacher: Torben Bach Pedersen