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MLOpsLaajuus (5 op)

Tunnus: MS00CN45

Laajuus

5 op

Osaamistavoitteet

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

Sisältö

- Process and tools for Machine Learning Engineering for Production (MLOps)

Ilmoittautumisaika

02.12.2024 - 31.12.2024

Ajoitus

01.01.2025 - 31.07.2025

Opintopistemäärä

5 op

Toteutustapa

Lähiopetus

Toimipiste

Kupittaan kampus

Opetuskielet
  • Suomi
  • Englanti
Paikat

10 - 30

Koulutus
  • Insinööri (ylempi AMK), Data Engineering and AI
  • Tradenomi (ylempi AMK), Data Engineering and AI
Opettaja
  • Jussi Salmi
Ryhmät
  • YINTES24
    Master of Engineering, Interactive Technologies
  • YINTBS24
    Master of Business Administration, Interactive Technologies
  • YDATIS24
    Insinööri (ylempi AMK), Data Engineering and AI
  • YDATTS24
    Tradenomi (ylempi AMK), Data Engineering and AI

Tavoitteet

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

Sisältö

- Process and tools for Machine Learning Engineering for Production (MLOps)

Oppimateriaalit

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.

Opetusmenetelmät

Contact learning, practical assignments, independent study

Opiskelijan ajankäyttö ja kuormitus

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

Sisällön jaksotus

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.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

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

Ilmoittautumisaika

02.12.2023 - 15.01.2024

Ajoitus

15.01.2024 - 30.04.2024

Opintopistemäärä

5 op

Toteutustapa

Lähiopetus

Yksikkö

Tekniikka ja liiketoiminta

Opetuskielet
  • Suomi
  • Englanti
Koulutus
  • Insinööri (ylempi AMK), Data Engineering and AI
  • Tradenomi (ylempi AMK), Data Engineering and AI
Opettaja
  • Tommi Tuomola
Ryhmät
  • YDATIS23
    Insinööri (ylempi AMK), Data Engineering and AI
  • YDATTS23
    Tradenomi (ylempi AMK), Data Engineering and AI

Tavoitteet

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

Sisältö

- Process and tools for Machine Learning Engineering for Production (MLOps)

Oppimateriaalit

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.

Opetusmenetelmät

Contact learning, practical assignments, independent study

Opiskelijan ajankäyttö ja kuormitus

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

Sisällön jaksotus

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.

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

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