Cloud Computing (5 cr)
Code: R504TL114-3003
General information
Enrollment
01.10.2024 - 12.01.2025
Timing
13.01.2025 - 04.05.2025
Credits
5 op
Mode of delivery
Contact teaching
Unit
Bachelor of Engineering, Information Technology
Teaching languages
- English
Seats
0 - 50
Degree programmes
- Degree Programme in Information and Communication Technology
Teachers
- Matias Hiltunen
- Mikko Pajula
Responsible person
Mikko Pajula
Student groups
-
R54T22SBachelor of Engineering, Information Technology (full time day studies), autumn 2022
Objective
The student is able to utilize the possibilities of cloud computing services in application development. The student has an overview of different cloud computing services and their features. The student is able to choose a suitable cloud computing service, which may be related to data management, image recognition, text conversion, video processing or other cloud computing.
Content
The course covers various aspects of Cloud Computing and usage examples:
- Deployment of a service provider's Cloud Computing solution
- Identify an object from an image using a cloud computing service
- Processing the text and identifying things from the text
- Programming in the cloud computing service
- Data management using a cloud computing service
- Comparison of cloud computing solutions
- Using a solution that utilizes cloud computing on a mobile device
Materials
Materials will be compiled and made available in the Moodle workspace. If necessary, additional resources and materials will be acquired and added from the internet. This includes documentation and guides for the cloud services used in the course.
Self-study prerequisites, if not familiar: Basics of Python: Familiarity with the basic concepts and syntax of the Python programming language. Basics of Data Management: Understanding of fundamental aspects of data handling, including JSON and databases. Basics of Information Technology: Knowledge of key IT concepts, including understanding what a CPU and GPU are.
Teaching methods
In-person teaching and support for exercises during classes, as well as online materials to support the completion of tasks.
Content scheduling
Theory: Introduction to IaaS, PaaS, and other cloud service options.
Basics and introduction to cloud computing.
Cloud computing: Machine learning in the cloud (Google's Teachable Machine: An interactive tool for machine learning, Google Colab, and possibly other tools), focusing on image recognition and natural language processing.
Basics of cloud platforms and cloud deployment.
Introduction to Firebase/Supabase or other similar technologies.
Ethical and sustainable approaches, covering GDPR, energy consumption, and cost savings.
Evaluation scale
H-5
Assessment criteria, satisfactory (1)
The student understands the principles of cloud computing and cloud service and is able to use cloud computing in simple implementations. The student is able to solve basic problems and search for more information from the digital materials of cloud computing service providers.
Assessment criteria, good (3)
The student understands the principles of cloud computing and cloud service and is able to plan and implement implementations that utilize cloud computing.
The student is able to utilize the features of the cloud computing service. The student is able to choose the most effective solution for a defined need from the services of several cloud computing service providers. The student is able to implement a solution that works with the selected service.
The student is able to solve problems and is able to search for more information from the digital materials of cloud computing service providers.
Assessment criteria, excellent (5)
The student is able to use the cloud computing service smoothly as part of an information system. The student is able to choose the most suitable cloud computing solution for the task. The student is able to apply the features of the cloud computing service in many ways. The student is able to utilize the features of the cloud computing service in software development.
Assessment methods and criteria
Grades are based on the quality, quantity, and comprehensiveness of the exercises.