Cloud-Based Machine Learning (5cr)

Evaluation scale

H-5

Content scheduling

- Cloud computing in general
- Common cloud environments
- Basics of neural networks and deep learning
- Technological considerations in deep learning
- Basics of image classification and object detection
- Machine learning coding in the cloud
- Other cloud computing related tools

Objective

The student is familiar with common machine learning models supported by online platforms for machine learning. The student can create custom machine learning applications in the cloud without having to write complex code.

Content

Machine learning models supported by the largest cloud providers: binary prediction, category prediction and value prediction.

Building, training and deploying machine learning model in the cloud:
- Preparing the data
- Training the model to learn from the data
- Deploying the model
- Evaluate the model's performance

Location and time

The course will be studied on-campus. Lapland University of Applied Sciences, Rantavitikka Campus, 12.1.2026 - 15.5.2026.

Materials

The study material of the course will be in the course's Moodle workspace. Additional material can be searched from the internet as needed. It's also recommended to independently search for more information during the course.

Teaching methods

Lectures, workshops, examples, exercises and self-supervised work.

The Arene's AI recommendations and traffic light model for universities of applied sciences will be applied during the course. Whether AI is allowed during an exercise, will depend on the exercise itself.

Employer connections

The study unit has been connected to the semester project held during the same semester. The technologies and related examples during the course will be partially based on the needs of the involved company in the semester project.

Exam schedules

The course will be graded based on personal work and exercises.

International connections

The study unit will be in English.

Completion alternatives

Alternative ways to study the course can be inquired from the instructor.

Student workload

The study unit is 5 ECTS, which amounts to 135 hours of work in total (approximately). ON average, your work will be distributed the following way:

- Lectures and workshops: 50 h
- Self-study and preparation: 45 h
- Graded tasks: 40h

Assessment criteria, satisfactory (1)

The student can implement a machine learning application in the cloud.

Assessment criteria, good (3)

The student can implement machine learning applications in the cloud using different models.

Assessment criteria, excellent (5)

The student can implement machine learning applications in the cloud using different models and cloud machine learning services.