Machine learning components are essential for today's software systems, causing a need to
adapt traditional software engineering practices when developing machine-learning-based …

The rapid advancement of large language models (LLMs) has revolutionized artificial
intelligence, introducing unprecedented capabilities in natural language processing and …

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 …

The explorative and iterative nature of developing and operating ML applications leads to a
variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software …

Data processing pipelines that are designed to clean, transform and alter data in preparation
for learning predictive models, have an impact on those models' accuracy and performance …

The recent success of machine learning (ML) has led to an explosive growth of systems and
applications built by an ever-growing community of system builders and data science (DS) …

Machine Learning (ML) is increasingly used to automate impactful decisions, and the risks
arising from this wide-spread use are garnering attention from policy makers, scientists, and …

Machine learning (ML) and data science workflows are inherently exploratory. Data
scientists pose hypotheses, integrate the necessary data, and run ML pipelines of data …

Software organizations are increasingly incorporating machine learning (ML) into their
product offerings, driving a need for new data management tools. Many of these tools …

Abstract Machine learning (ML) has already fundamentally changed several businesses.
More recently, it has also been profoundly impacting the computational science and …