MLOps is essential to streamline the machine learning (ML) development process, ensure
ML models stay operational, and provide users with the desired value. MLOps enhances the …

Purpose Recent advancements in Artificial Intelligence (AI) and, at its core, Machine
Learning (ML) offer opportunities for organizations to develop new or enhance existing …

Abstract The field of MLOps (Machine Learning Operations), which focuses on effectively
managing and operationalizing ML workflows, has grown because of the advancements in …

Advancements in hardware and software have propelled machine learning (ML) solutions to
become vital components of numerous information systems. This calls for research on the …

Developing and operating AI systems based on machine learning (ML) has unique
challenges that render traditional practices inappropriate (eg, managing data drift). To that …

Data Science (DS) has gained increased relevance due to the potential to extract useful
insights from data. Quite commonly, this involves the utilization of Machine Learning (ML) …

The paper presents how organizational practices enable the improvement and maintenance
of task performance in a learning human-in-the-loop system exposed to a wide range of …

The commercial diffusion of machine learning (ML) enables the development of novel and
previously unattainable organizational capabilities. Over the past ten years, the rapid …

Abstract The area of Business Intelligence and Analytics (BIA) has repeatedly oscillated
between more central, efficiency-oriented, professionalized approaches and decentral …

The recent advances in mobile robot surveillance have significantly enhanced supervision
tasks. Unlike traditional CCTV systems, these robots improve patrolling capabilities …