… Working on machine learning applications is hard enough. Having mismatched dev and test
… But if your goal is to make progress on a specific machine learning application rather than …

Abstract—Machine learning techniques have deeply rooted in our everyday life. However, …
in every aspect of machine learning. To make machine learning techniques easier to apply …

… chapter uses Microsoft’s popular machine learning … software engineering approaches to
machine learning as well as the new area of software analytics which focuses on the application …

… machine learning and conventional learning approaches and the major challenges ahead.
The … , a comprehensive survey of the major applications of deep learning covering variety of …

… Finally, other examples of DSMLs have been proposed for Quantum Machine Learning [74] …
integration, enabling software engineers to design hybrid applications without delving into …

… But, in this study, they have uses IRI-proxy calculation to measure the road conditions and
they … This project has used the iterative software development methodology (Chapter 3). So, …

… To keep up with all these advances, software engineering has come a long way since its …
because ML presents novel tools to predict outcomes and, in the case of software testing, this …

… application areas of machine learning and its contribution to society/industry. In addition to
discussing the multifarious applications of machine learning… bolts of machine learning and the …

… (ML) is used increasingly in realworld applications. In this paper, we describe our ongoing …
, ML engineering constitutes a paradigm shift compared to conventional software engineering. …

… for the development of machine learning applications, covering … respective quality during the
software development process. This … This paper puts forward CRISP-ML(Q), to path the way …