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Implementing IoT PipelineLaajuus (15 cr)

Code: AT00CK36

Credits

15 op

Teaching language

  • English

Objective

The student is able to
- create a scalable restful API-services for IoT data
- implement ML and AI operations also in edge devices
- use appropriate databases to store IoT data in the platform
- work as a team leader in an ICT project and a member in a multidisciplinary project

Enrollment

20.11.2024 - 03.01.2025

Timing

01.01.2025 - 30.06.2025

Number of ECTS credits allocated

15 op

Mode of delivery

Contact teaching

Unit

Faculty of Technology (LAB)

Campus

Lappeenranta Campus

Teaching languages
  • English
Degree programmes
  • Bachelor's Degree Programme in Industrial Information Technology
Teachers
  • Mira Vorne
  • Jyrki Antikainen
Scheduling groups
  • Luennot 1 (Size: 500. Open UAS: 0.)
Groups
  • TLPRIIT23S
  • TLPRIIT22S
Small groups
  • Lecture 1

Learning outcomes

The student is able to
- create a scalable restful API-services for IoT data
- implement ML and AI operations also in edge devices
- use appropriate databases to store IoT data in the platform
- work as a team leader in an ICT project and a member in a multidisciplinary project

Timing and attendance

Attending classes is required according to the schedules defined in the Time-edit.

Learning material and recommended literature

Material shared in the Moodle platform, presented during the lessons, www*.

These will be clarified during the class hours.

Learning environment

Lappeenranta Campus & Moodle

Student time use and work load

The scope of the course is 15 ECTS, which corresponds to an average of 412 hours of studying. This includes the defined lectures, self-studying and exercises.

Contents

- Scalable RESTful API-services for IoT Data
- ML and AI operations also in edge devices
- Databases in scalable environments
- Team leading in ICT project and a member in multidisciplinary project

Additional information for students: previous knowledge etc.

Deep understanding of the IoT Pipeline basics
- Embedded systems
- Able to manage single VPS instance via SSH
- Secure communication over HTTPS
- Able to build backend services and integrate them to databases

Assessment scale

1-5

Failed (0)

Not able to reach level 1 criteria.

Assessment criteria: level 1 (assessment scale 1–5)

The student and the group have been able to cooperate productively and document their work. The student and the group are able to present their output to other students as instructed. The output mainly meets the criteria of the assignments.

Assessment criteria: level 3 (assessment scale 1–5)

The student and group deliverables (for example, presentations) are clear and logical.

They clearly show the implementation in accordance with the tasks given and any deviations have been reported. The student's and group's deliverables reflect an understanding of the work, e.g. the terms and professional words are correct. The student and group members also talk about what they have learned and solutions to challenges (reflection).

Assessment criteria: level 5 (assessment scale 1–5)

Student and group deliverables (for example, presentations) are clear and logical.

The student's and group's deliverables also contain a link to the theory of the course, and you can compare and justify with facts the solutions that have been used in the work. The student and the group have been able to add something extra they came up with to the work. The presentations are illustrative and things are explained with the help of examples

Enrollment

01.12.2022 - 04.01.2023

Timing

09.01.2023 - 28.04.2023

Number of ECTS credits allocated

15 op

Mode of delivery

Contact teaching

Unit

Faculty of Technology (LAB)

Campus

Lappeenranta Campus

Teaching languages
  • English
Degree programmes
  • Bachelor's Degree Programme in Industrial Information Technology
Teachers
  • LAB_virtuaalihenkilö_TVT_01 Virtuaaliopettaja (LAB)
  • Karri Miettinen
  • Jyrki Antikainen
Scheduling groups
  • Luennot 1 (Size: 500. Open UAS: 0.)
Groups
  • TLPRIIT21S
Small groups
  • Luennot 1

Learning outcomes

The student is able to
- create a scalable restful API-services for IoT data
- implement ML and AI operations also in edge devices
- use appropriate databases to store IoT data in the platform
- work as a team leader in an ICT project and a member in a multidisciplinary project

Assessment scale

1-5