Engineering successful machine learning (ML)-enabled systems poses various challenges
from both a theoretical and a practical side. Among those challenges are how to effectively …

The increasing adoption of natural language processing (NLP) models across industries has
led to practitioners' need for machine learning (ML) systems to handle these models …

Machine learning (ML) components are increasingly incorporated into software products, yet
developers face challenges in transitioning from ML prototypes to products. Academic …

We identify and describe episodes of sensemaking around challenges in modern Artificial-
Intelligence (AI)-based systems development that emerged in projects carried out by IBM …

Politicians and care associations advocate for the use of machine learning (ML) systems to
improve the delivery of adult social services. Yet, guidance on how to implement ML systems …

Abstract [Context] Systems that incorporate Machine Learning (ML) models, often referred to
as ML-enabled systems, have become commonplace. However, empirical evidence on how …

The rise of generative AI has led many companies to hire freelancers to harness its potential.
However, this technology presents unique challenges to developers who have not …

Machine learning (ML)-based software systems are rapidly gaining adoption across various
domains, making it increasingly essential to ensure they perform as intended. This report …

Context: Machine learning (ML)-enabled systems are being increasingly adopted by
companies aiming to enhance their products and operational processes. Objective: This …

Anomaly detection techniques are essential in automating the monitoring of IT systems and
operations. These techniques imply that machine learning algorithms are trained on …