Artificial intelligence (AI) systems are trained to solve complex problems and learn to
perform specific tasks by using large volumes of data, such as prediction, classification …

Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

Defining and evaluating data quality can be a complex task as it varies depending on the
specific purpose for which the data is intended. To effectively assess data quality, it is …

It is widely accepted that data preparation is one of the most time-consuming steps of the
machine learning (ML) lifecycle. It is also one of the most important steps, as the quality of …

Machine Learning (ML) is commonly used to automate decisions in domains as varied as
credit and lending, medical diagnosis, and hiring. These decisions are consequential …

Software traceability links are established between diverse artifacts of the software
development process in order to support tasks such as compliance analysis, safety …

Software systems that learn from data with AI and machine learning (ML) are becoming
ubiquitous and are increasingly used to automate impactful decisions. The risks arising from …

Complex data pipelines are increasingly common in diverse applications such as BI
reporting and ML modeling. These pipelines often recur regularly (eg, daily or weekly), as BI …