The automotive industry is undergoing a profound digital transformation to create
autonomous vehicles. Vehicle-to-Everything (V2X) communications enable the provisioning …

Early fault detection, both in equipment and the products in process, is of paramount
importance in industrial processes to ensure the quality of the final product, avoid abnormal …

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 …

Organizations rely on machine learning engineers (MLEs) to operationalize ML, ie, deploy
and maintain ML pipelines in production. The process of operationalizing ML, or MLOps …

Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators
(DSA) are used to train increasingly-complex deep learning models. These clusters rely on a …

HCI engages with data science through many topics and themes. Researchers have
addressed biased dataset problems, arguing that bad data can cause innocent software to …

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 …

One of the key challenges in fully embracing machine learning (ML) in civil and
environmental engineering revolves around the need for coding (or programming) …

Organizations rely on machine learning engineers (MLEs) to deploy models and maintain
ML pipelines in production. Due to models' extensive reliance on fresh data, the …

Artificial intelligence (AI) and machine learning (ML) techniques have existed for years, but
new hardware trends and advances in model training and inference have radically improved …