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Title:Samodejno strojno učenje znotraj relacijske podatkovne baze PostgreSQL
Authors:ID Šek, Aljaž (Author)
ID Fister, Iztok (Mentor) More about this mentor... New window
ID Vrbančič, Grega (Comentor)
Files:.pdf VS_Sek_Aljaz_2024.pdf (2,22 MB)
MD5: 296A6D50EC6FC5009B5DE1291FDD967B
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomskem delu smo predstavili in raziskovali uporabnost integracije samodejnega strojnega učenja znotraj relacijske podatkovne baze PostgresSQL. Osredotočili smo se na primerjavo dveh odprtokodnih orodij, ki to integracijo omogočata, in sicer MindsDB in PostgresML, kateri smo primerjali na praktičnem primeru prodaje vozil. Prikazali smo celoten postopek namestitve, povezovanje s podatkovno bazo in uporabo obeh orodij za gradnjo modela strojnega učenja. Raziskali smo glavne razlike med obema orodjema in na koncu predstavili prednosti in slabosti ter ugotovitve primerjave.
Keywords:strojno učenje, samodejno strojno učenje, strojno učenje znotraj relacijskih podatkovnih baz, MindsDB, PostgresML
Place of publishing:Maribor
Publisher:[A. Šek]
Year of publishing:2024
PID:20.500.12556/DKUM-90447 New window
UDC:004.85:004.652.4(043.2)
COBISS.SI-ID:221162499 New window
Publication date in DKUM:23.12.2024
Views:235
Downloads:77
Metadata:XML DC-XML DC-RDF
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:04.09.2024

Secondary language

Language:English
Title:Automated Machine Learning within the PostgreSQL Relational Database
Abstract:In the thesis, we presented and explored the usability of integrating automated machine learning within the PostgreSQL realtional database. Our main focus was on comparing two open-source tools that enable this integration, namely MindsDB in PostgresML, both of which we compared on a practical example of vehicle sales. We demonstrated the entire process of installation, connection to the database, and the use of both tools for building a machine learning model. We examined the main differences between the two tools and, in the end, presented the advantages and disadvantages as well as the findings of the comparison.
Keywords:machine learning, automated machine learning, machine learning within a relational database, MindsDB, PostgresML


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