Uporabniška izkušnja in zmogljivost platform za strojno učenje: pregled in primerjava

Kramberger, Tomaž

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Title:	Uporabniška izkušnja in zmogljivost platform za strojno učenje: pregled in primerjava
Authors:	ID Kramberger, Tomaž (Author) ID Žlahtič, Bojan (Mentor) More about this mentor...
Files:	VS_Kramberger_Tomaz_2025.pdf (4,78 MB) MD5: 4368B52E7CADECD00D27325135B9A0F8
Language:	Slovenian
Work type:	Bachelor thesis/paper
Typology:	2.11 - Undergraduate Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	V tej diplomski nalogi smo analizirali in primerjali štiri vodilne platforme za strojno učenje: Google Vertex AI, AWS SageMaker, Azure Machine Learning in Databricks. Osredotočili smo se na njihovo zmogljivost pri treniranju modelov, avtomatizaciji procesov ter uporabniško izkušnjo. Uporabili smo tri različne tipe nalog strojnega učenja: klasifikacijo, regresijo in gručen z uporabo ustreznih javno dostopnih podatkovnih zbirk. Testiranja smo izvedli z uporabo Jupyter zvezkov, ročno hiperparametrizacijo in funkcijami AutoML, kar nam je omogočilo primerjavo med različnimi pristopi na vsaki platformi. Na podlagi analize rezultatov ter uporabniških izkušenj smo izpostavili prednosti in slabosti vsake platforme. Ugotovili smo, da je izbira najbolj primerne platforme odvisna od ciljev, tehničnega znanja uporabnika ter specifičnih zahtev projekta.
Keywords:	strojno učenje, platforma za strojno učenje, uporabniška izkušnja, umetna inteligenca, zmogljivost platform za strojno učenje
Place of publishing:	Maribor
Year of publishing:	2025
PID:	20.500.12556/DKUM-95706
Publication date in DKUM:	22.12.2025
Views:	0
Downloads:	5
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Categories:	KTFMB - FERI
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Licences

License:	CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:	The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:	13.10.2025

Secondary language

Language:	English
Title:	User experience and performance of machine learning platforms: a review and comparison
Abstract:	This thesis presents an in-depth analysis and comparison of four leading machine learning platforms: Google Vertex AI, AWS SageMaker, Azure Machine Learning, and Databricks. The focus was placed on evaluating their performance in model training, process automation, and user experience. Three different types of machine learning tasks—classification, regression, and clustering—were explored using relevant publicly available datasets. The evaluation was conducted using Jupyter notebooks, manual hyperparameter tuning, and AutoML functionalities, allowing for a comprehensive comparison of various approaches on each platform. Based on the results and user experience assessments, we highlighted the strengths and weaknesses of each solution. The study concludes that the choice of the most suitable platform largely depends on user expertise, project goals, and technical requirements.
Keywords:	Machine Learning, Platforms for machine learning, User experience, artificial intelligence, performance of machine learning platforms

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