Loschi, Lorenzo
(2022)
Methods for monitoring Machine Learning models.
[Laurea magistrale], Università di Bologna, Corso di Studio in
Artificial intelligence [LM-DM270], Documento full-text non disponibile
Il full-text non è disponibile per scelta dell'autore.
(
Contatta l'autore)
Abstract
The following thesis aims to investigate the issues concerning the maintenance of a Machine Learning model over time, both about the versioning of the model itself and the data on which it is trained and about data monitoring tools and
their distribution. The themes of Data Drift and Concept Drift were then explored and the performance of some of the most popular techniques in the field of Anomaly detection, such as VAE, PCA, and Monte Carlo Dropout, were evaluated.
Abstract
The following thesis aims to investigate the issues concerning the maintenance of a Machine Learning model over time, both about the versioning of the model itself and the data on which it is trained and about data monitoring tools and
their distribution. The themes of Data Drift and Concept Drift were then explored and the performance of some of the most popular techniques in the field of Anomaly detection, such as VAE, PCA, and Monte Carlo Dropout, were evaluated.
Tipologia del documento
Tesi di laurea
(Laurea magistrale)
Autore della tesi
Loschi, Lorenzo
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Anomaly Detection,Machine Learning,Model Monitoring,Model Versioning,Data Drift
Data di discussione della Tesi
6 Dicembre 2022
URI
Altri metadati
Tipologia del documento
Tesi di laurea
(NON SPECIFICATO)
Autore della tesi
Loschi, Lorenzo
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Anomaly Detection,Machine Learning,Model Monitoring,Model Versioning,Data Drift
Data di discussione della Tesi
6 Dicembre 2022
URI
Gestione del documento:
Login