This book chapter delves into the dynamics of continual learning, which is the process of
incrementally learning from a non-stationary stream of data. Although continual learning is a …

Predictive machine learning models nowadays are often updated in a stateless and
expensive way. The two main future trends for companies that want to build machine …

Machine learning (ML) is often applied in use cases where training data evolves and/or
grows over time. Training must incorporate data changes for high model quality, however …

Machine learning training data is often dynamic in real-world use cases, ie, data is added or
removed and may experience distribution shifts over time. Models must incorporate this …

Recently, researchers have seen promising results in using serverless computing for real-
time machine learning inference tasks. Several researchers have also used serverless for …

Machine learning training data is often dynamic in real-world use cases, ie, data is added or
removed and may experience distribution shifts over time. Models must incorporate this …

This research aims to qualitatively analyze the use of digital tools for political participation in
digital democracy using 10 case studies. Technological development is influencing every …