Machine Learning Engineering

Course guarantor:

Jan Faigl

Lecturer:

Tomáš Báča, Jan Faigl

Tutor:

Tomáš Báča

Supervisor:

Department of Cybernetics

Synopsis:

The course focuses on anchoring theoretical knowledge of artificial intelligence (AI) methods into practice. Upon completion of the course, students should gain a practical understanding of the principles and considerations of applying machine learning to real-world problems. They should get familiar with technologies and workflows that allow them to actionize knowledge acquired throughout the program. The student's work is oriented to the programming language Python, with the option to use C++, Julia, and Rust. During the labs and homework, students become familiar with topics like training pipelines, containerization, and production deployments.

Requirements:

Syllabus of lectures:

Overview of the learning approach and its deployment in the production

End-to-end learning, data management, and dataset versioning

Designing training pipeline and its deployments

Virtualization and Cloud Computing

Containerization (Docker, Singularity)

Deployment Patterns and Tools

Distributed learning mechanisms

Continuous Integration and Deployment (CI/CD) tools

Syllabus of tutorials:

During the labs, the students practice the topics discussed in the lectures to collect hands-on experience with the technology and acquire the desired skills. The homework tasks are introduced, and students work on them.

Study Objective:

The course focuses on anchoring theoretical knowledge of artificial intelligence (AI) methods into practice. Upon completion of the course, students should gain a practical understanding of the principles and considerations of applying machine learning to real-world problems. They should get familiar with technologies and workflows that allow them to actionize knowledge acquired throughout the program. The student's work is oriented to the programming language Python, with the option to use C++, Julia, and Rust. During the labs and homework, students become familiar with topics like training pipelines, containerization, and production deployments.

Study materials:

* Andrew P. MacMahon: Machine Learning Engineering with Python - Second Edition (nebo novější), 2023. GitHub - PacktPublishing/Machine-Learning-Engineering-with-Python-Second-Edition

* Stas Bekman: Machine Learning Engineering Open Book - https://github.com/stas00/ml-engineering

* Geoff Hulten: Building Intelligent Systems: A Guide to Machine Learning Engineering, Apress. 2018.

Note:

Time-table for winter semester 2025/2026:

Time-table is not available yet

Time-table for summer semester 2025/2026:

Time-table is not available yet

The course is a part of the following study plans:

• prg.ai Master (compulsory course in the program)