Subject

Automation of Machine Learning Processes

Course Title

Automation of Machine Learning Processes
Automated machine learning

Code

F23L3S163

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

6 / Summer

Number of ECTS credits

Teacher

Biljana Tojtovska Ribarski, Bojan Ilijoski, Panche Ribarski

Prerequisites for enrolling in the course

Algorithms and Data Structures or Applied Algorithms and Data Structures

10.

Objectives of the course programme (competences)

Introduction to the fundamental steps for deploying machine-learning models in production, optimising ML pipelines, designing the complete lifecycle of ML models, CI/CD for ML, managing ML code, monitoring models in production, and model management.

11.

Course content

Lectures:
1. 1. Data extraction, transformation and loading
2. 2. Data flow and streaming
3. 3. ML automation – code management
4. 4. ML automation – model management
5. 5. ML automation – process management
6. 6. Model logging
7. 7. Model monitoring
8. 8. Model serving
9. 9. Continuous integration and continuous development
10. 10. Testing
11. 11. Final project

Practical Classes:
1. 1. Data extraction, transformation and loading
2. 2. Data flow and streaming
3. 3. ML automation – code management
4. 4. ML automation – model management
5. 5. ML automation – process management
6. 6. Model logging
7. 7. Model monitoring
8. 8. Model serving
9. 9. Continuous integration and continuous development
10. 10. Testing
11. 11. Final project

12.

Learning methods

Lectures, tutorial classes, laboratory classes, project assignments, homework, and development of software packages for automating machine-learning processes

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	45 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	75 hours

17.

Assessment method

17.1.	Tests	40 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	20 points
17.4.	Final exam	100 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

—

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.

Required literature

1. Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann | Introducing MLOps: How to Scale Machine Learning in the Enterprise | O`reilly | 2020
2. Emmanuel Raj | Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale | Packt Publishing | 2021
3.

22.2.

Additional literature

No. Author Title Publisher Year