Machine Learning Operations and Prescriptive Analytics

1.

Subject title

Machine Learning Operations and Prescriptive Analytics

Операционализирање на машинско учење и прескриптивна аналитика

2.

Code

m23_s_056

3.

Study program

Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Eco-informatics, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Statistics and Data Analytics, Software for embedded systems, Software Engineering, Cloud Computing, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Еducation with ICT, IT management, Software Engineering, Data science in computer science and engineering,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

ворн. проф. д-р Ефтим Здравевски ворн. проф. д-р Петре Ламески

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

This course in advanced data mining will cover modern predictive and prescriptive analytics algorithms and techniques. The course will prioritize application over theory and concentrate on real-world issues and applications to deliver ML findings that can be put into practice. This course bridges the gap between enterprise production deployment and machine learning projects/experiments. To do that, the course will discuss what Machine Learning Operations (MLOps) is and why it`s crucial for deploying machine learning projects in corporate production settings. As a result, model engineering and deployment engineering ideas, tools, and platforms will be introduced to the students. The course covers the three MLOps pillars of software engineering, model engineering, and deployment engineering. Software engineering topics covered include software architecture, Continuous Integration/Continuous Delivery, and data versioning. In addition, the course focuses on industry best practices that are crucial for deploying machine learning projects in enterprises. A student who successfully completes this course will be able to explain the machine learning lifecycle and what is necessary to take an idea from conception to operationalization in an organizational setting. Additionally, students are exposed to cutting-edge MLOps platforms and technologies, including Allegro AI, xpresso, Dataiku, LityxIQ, DataRobot, AWS Sagemaker, Jenkins, Slack, Docker, Kubernetes, etc.

11.

Subject content:

Differences in business analytics: descriptive, predictive and prescriptive; Designing prescriptive platforms, modeling situations, and developing alternatives; Data engineering focused on AI: principles and practice; Defining MLOps and its challenges; Defining key features of MLOps: model development, productionalization, deployment, monitoring, iterating, life cycle management; Steps for preparing for production; Reproducibility and auditability; Security and governance of ML models; Mitigating risk of ML models; Strategies for deployment; Scalability and containerization; Monitoring and feedback loop; Model drift detection; Achieving business value from AI and ML.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

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

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

15 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

45 hours

17.

Grading method

17.1.

Tests

30 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

10 points

17.4.

Final exam

0 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.

Condition for signature and taking final exam

Реализирани активности 15.1 и 15.2

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature