Study Guide

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.

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.

Teaching methods

Contact learning, practical assignments, independent study

Student workload

Contact hours 16 h
Inpendent studying 119h, including:
- Studying the course material
- Completing assignments
- Project

Content scheduling

After completing the course, the students can
- describe and understand how MLOps projects operate

Process and tools for Machine Learning Engineering for Production (MLOps)
Scheduling:Chosen topics from MLops, Dataops and Devops for artificial intelligence and data engineering students.

Evaluation scale

H-5

Assessment methods and criteria

Assignments returned throughout the course
Small project at the end of the course