Cyber Security (Master) (without Thesis) (English) Preview
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code:

AO5018

Course Name:

Machine Learning Operations

Semester:

Spring

Course Credits:

ECTS

Language of instruction:

English

Course Condition:

Does the Course Require Work Experience?:

Yes

Type of course:

Departmental Elective

Course Level:

Master

TR-NQF-HE:7. Master`s Degree

QF-EHEA:Second Cycle

EQF-LLL:7. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator:

Dr. Öğr. Üy. ALPER ÖNER

Course Lecturer(s):

Asst. Prof. Alper Öner

Course Assistants:

Course Objective and Content

Course Objectives:	• To provide the basic concepts of MLOps, • To give an ability to form well-defined problem formulations for MLOps problems, • To give an ability to solve well-defined MLOps problems by using appropriate programming tools, • To give an ability to design basic MLOps Problems, • To give an ability to work together with colleagues in a MLOps project.
Course Content:	After completing this course satisfactorily, a student will: 1. Design a well-defined problem formulation for a basic MLOps problem. 2. Solve well-defined problems using MLOps methods and algorithms. 3. Explain basic concepts of MLOps methods. 4. Develop MLOps systems by programming languages. 5. Work as a team in a MLOps project.

Learning Outcomes

The students who have succeeded in this course;
1) Gaining the ability to manage the model development process.
2) Gaining the ability to manage the model distribution and integration process.
3) Gaining the ability to perform performance and error analysis.
4) Models will be able to scale in the real world.
5) Gaining the ability to improve and maintain models in the real world.

Course Flow Plan

Week	Subject	Related Preparation
1)	What is MLOps?
2)	MLOps Design Patterns - Data
3)	MLOps Design Patterns - Problem
4)	MLOps Design Patterns - Model
5)	MLOps Design Patterns - Serving
6)	Kubernetes
7)	Data Distribution Drift
8)	Responsible AI
9)	Monitoring and Observability
10)	Project Proposal Presentation
11)	Continuous Integration
12)	Continuous Delivery
13)	Continuous Test
14)	MLOps Applications

Sources

Course Notes / Textbooks:	Machine Learning Design Patterns Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan, Sara Robinson, Michael Munn.
References:	Designing Machine Learning Systems An Iterative Process for Production- Ready Applications (Chip Huyen). Reliable Machine Learning Applying SRE Principles to ML in Production (Cathy Chen, Niall Murphy, Kranti Parisa etc.)

Course - Program Learning Outcome Relationship

Course Learning Outcomes	1	2	3	4	5
Program Outcomes
1) Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications.
2) To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
3) To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines.
4) To be able to solve the problems encountered in the field by using research methods.
5) To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data.
6) To be able to communicate orally and in writing using a foreign language at least at the B2 General Level of the European Language Portfolio.
7) To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.
8) To be able to use information and communication technologies at an advanced level along with computer software at the level required by the field.
9) To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field.
10) To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.
11) Being able to independently carry out a work that requires expertise in the field.
12) To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Average	3 Highest

	Program Outcomes	Level of Contribution
1)	Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications.
2)	To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
3)	To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines.
4)	To be able to solve the problems encountered in the field by using research methods.
5)	To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data.
6)	To be able to communicate orally and in writing using a foreign language at least at the B2 General Level of the European Language Portfolio.
7)	To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.
8)	To be able to use information and communication technologies at an advanced level along with computer software at the level required by the field.
9)	To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field.
10)	To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.
11)	Being able to independently carry out a work that requires expertise in the field.
12)	To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility.

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
total		%
PERCENTAGE OF SEMESTER WORK		% 0
PERCENTAGE OF FINAL WORK		%
total		%

Workload and ECTS Credit Calculation

Activities	Number of Activities	Workload
Course Hours	13	52
Presentations / Seminar	2	20
Project	3	25
Homework Assignments	4	40
Final	3	25
Total Workload		162