MLOps (5 cr)
Code: MS00CN45-3001
General information
Enrollment
02.12.2023 - 15.01.2024
Timing
15.01.2024 - 30.04.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Engineering and Business
Teaching languages
- Finnish
- English
Degree programmes
- Master of Engineering, Data Engineering and AI
- Master of Business Administration, Data Engineering and AI
Teachers
- Tommi Tuomola
Groups
-
YDATIS23
-
YDATTS23
- 15.01.2024 08:30 - 11:45, Machine Learning Engineering for Production MS00CN45-3001
- 16.01.2024 12:30 - 16:00, MLOps MS00CN45-3001
- 13.02.2024 12:30 - 16:00, MLOps MS00CN45-3001
- 12.03.2024 12:30 - 16:00, MLOps MS00CN45-3001
- 16.04.2024 12:30 - 16:00, MLOps MS00CN45-3001
Objective
After completing the course, the students can
- describe and understand how MLOps projects operate
Content
- Process and tools for Machine Learning Engineering for Production (MLOps)
Materials
Teacher provided lecture material
Supporting public online material
Teacher provided virtual machines
All needed material (or at least a link to them) will be available in itslearning.
Teaching methods
Contact learning, practical assignments, independent study
International connections
Given examples and exercises support each topic studied during the lectures. Additional material in the form of tutorials and reliable information sources is provided.
Student workload
Contact hours 16 h
Inpendent studying 119h, including:
- Studying the course material
- Completing assignments
- Project
Content scheduling
Chosen topics from MLops, Dataops and Devops for artificial intelligence and data engineering students.
Further information
Itslearning and contact classes are the main communication channels used on this course.
The student is required to have a computer capable of running a simple Ubuntu virtual machine.
Evaluation scale
H-5
Assessment methods and criteria
Assignments returned throughout the course
Small project at the end of the course