The role of Machine Learning (ML) engineers in mobile development has become
increasingly important in recent years, as more and more business-critical mobile …

Smartphones are increasingly used as universal IoT gateways collecting data from
connected sensors in a wide range of industrial applications. With the increasing computing …

Following continuous software engineering practices, there has been an increasing interest
in rapid deployment of machine learning (ML) features, called MLOps. In this paper, we …

Mobile computing is becoming a key aspect of our lives. This article builds on conversations
held during the 2018 IEEE/ACM International Conference on Mobile Software Engineering …

In many machine learning projects, the lack of an effective monitoring system is a worrying
issue. This leads to a series of challenges and risks that compromise the quality, reliability …

Mobile apps increasingly incorporate machine learning (ML) to enhance their services.
However, integrating ML models locally with mobile apps can be challenging. Each ML …

The adoption of continuous software engineering practices such as DevOps (Development
and Operations) in business operations has contributed to significantly shorter software …

Background. The rapid and growing popularity of machine learning (ML) applications has
led to an increasing interest in MLOps, that is, the practice of continuous integration and …

With the increasing trend of Machine Learning (ML) enabled software applications, the
paradigm of ML Operations (MLOps) has gained tremendous attention of researchers and …

The widespread distribution of smart mobile devices offers promising perspectives for the
timely collection of huge amounts of data. When realizing sophisticated mobile data …