Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

Companies struggle to continuously develop and deploy Artificial Intelligence (AI) models to
complex production systems due to AI characteristics while assuring quality. To ease the …

Background: Software Package Registries (SPRs) are an integral part of the software supply
chain. These collaborative platforms unite contributors, users, and code for streamlined …

Today, diabetes is one of the most common, chronic, and, due to some complications,
deadliest diseases in the world. The early detection of diabetes is very important for its timely …

Context: In machine learning (ML) applications, assets include not only the ML models
themselves, but also the datasets, algorithms, and deployment tools that are essential in the …

Edge AI and Human Augmentation are two major technology trends, driven by recent
advancements in edge computing, IoT, and AI accelerators. As humans, things, and AI …

Recently we have observed emerging uses of deep learning techniques in multimedia
systems. Develo** a practical deep learning system is arduous and complex. It involves …

Currently, most machine learning models are trained by centralized teams and are rarely
updated. In contrast, open-source software development involves the iterative development …

Representation learning over dynamic graphs is critical for many real-world applications
such as social network services and recommender systems. Temporal graph neural …

Large-scale data analytics using machine learning (ML) underpins many modern data-
driven applications. ML systems provide means of specifying and executing these ML …