Add to ORKGThe 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
Supervised learning models are one of the most fundamental classes of models. Viewing supervised lea...
When machine learning models encounter data which is out of the distribution on which they were trai...
With the explosion of the number of distributed applications, a new dynamic server environment emerg...
Once a machine learning (ML) model is produced and used for commercial purposes, it is desirable to ...
The dynamicity of real-world systems poses a significant challenge to deployed predictive machine le...
Many crowdsensing applications today rely on learning algorithms applied to data streams to accurate...
Machine learning (ML) has become ubiquitous in various disciplines and applications, serving as a po...
As the use of machine learning techniques by organisations has become more common, the need for soft...
Machine learning systems both gained significant interest from the academic side and have seen adopt...
Machine learning and deep learning-based decision making has become part of today's software. The go...
Machine learning-based solutions are frequently adapted in several applications that require big dat...
The thesis develops machine learning algorithms for use in the Industry 4.0 concept. The main focus ...
Most machine learning models are trained on historical data to learn a static mapping between their ...
Machine learning extracts models from huge quantities of data. Models trained and validated over pas...
Monitoring machine learning models once they are deployed is challenging. It is even more challengin...
Supervised learning models are one of the most fundamental classes of models. Viewing supervised lea...
When machine learning models encounter data which is out of the distribution on which they were trai...
With the explosion of the number of distributed applications, a new dynamic server environment emerg...
Once a machine learning (ML) model is produced and used for commercial purposes, it is desirable to ...
The dynamicity of real-world systems poses a significant challenge to deployed predictive machine le...
Many crowdsensing applications today rely on learning algorithms applied to data streams to accurate...
Machine learning (ML) has become ubiquitous in various disciplines and applications, serving as a po...
As the use of machine learning techniques by organisations has become more common, the need for soft...
Machine learning systems both gained significant interest from the academic side and have seen adopt...
Machine learning and deep learning-based decision making has become part of today's software. The go...
Machine learning-based solutions are frequently adapted in several applications that require big dat...
The thesis develops machine learning algorithms for use in the Industry 4.0 concept. The main focus ...
Most machine learning models are trained on historical data to learn a static mapping between their ...
Machine learning extracts models from huge quantities of data. Models trained and validated over pas...
Monitoring machine learning models once they are deployed is challenging. It is even more challengin...
Supervised learning models are one of the most fundamental classes of models. Viewing supervised lea...
When machine learning models encounter data which is out of the distribution on which they were trai...
With the explosion of the number of distributed applications, a new dynamic server environment emerg...