Popis předmětu - BECM33MLE
Přehled studia |
Přehled oborů |
Všechny skupiny předmětů |
Všechny předměty |
Seznam rolí |
Vysvětlivky
Návod
| BECM33MLE | Machine Learning Engineering | ||
|---|---|---|---|
| Role: | P | Rozsah výuky: | 2P+2C |
| Katedra: | 13133 | Jazyk výuky: | EN |
| Garanti: | Faigl J. | Zakončení: | KZ |
| Přednášející: | Báča T., Brabec J., Lukány J. | Kreditů: | 6 |
| Cvičící: | Báča T., Pařil D. | Semestr: | Z |
Webová stránka:
https://cw.fel.cvut.cz/wiki/courses/becm33mle/Anotace:
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.Cíle studia:
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.Osnovy přednášek:
● 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) toolsOsnovy cvičení:
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.Literatura:
* 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.Požadavky:
Předmět je zahrnut do těchto studijních plánů:
| Plán | Obor | Role | Dop. semestr |
| MPPRGAI_2025 | Před zařazením do oboru | P | 1 |
| Stránka vytvořena 18.1.2026 12:51:16, semestry: Z/2027-8, L/2025-6, Z/2026-7, Z/2025-6, L/2026-7, připomínky k informační náplni zasílejte správci studijních plánů | Návrh a realizace: I. Halaška (K336), J. Novák (K336) |