7 citations · 19 across the 15 of their papers we have counts for
7 papers · 1 filter
Industrial Practices of Requirements Engineering for ML-Enabled Systems in Brazil
Antonio Pedro Santos Alves, Marcos Kalinowski, Daniel Mendez +4
[Context] In Brazil, 41% of companies use machine learning (ML) to some extent. However, several challenges have been reported when engineering ML-enabled systems, including unreal…
Naming the Pain in Machine Learning-Enabled Systems Engineering
Marcos Kalinowski, Daniel Mendez, Görkem Giray +12
Context: Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. Objective: This paper aims to…
Professional Insights into Benefits and Limitations of Implementing MLOps Principles
Gabriel Araujo, Marcos Kalinowski, Markus Endler +1
Context: Machine Learning Operations (MLOps) has emerged as a set of practices that combines development, testing, and operations to deploy and maintain machine learning applicatio…
Investigating the Impact of SOLID Design Principles on Machine Learning Code Understanding
Raphael Cabral, Marcos Kalinowski, Maria Teresa Baldassarre +3
[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly h…
On the Interaction between Software Engineers and Data Scientists when building Machine Learning-Enabled Systems
Gabriel Busquim, Hugo Villamizar, Maria Julia Lima +1
In recent years, Machine Learning (ML) components have been increasingly integrated into the core systems of organizations. Engineering such systems presents various challenges fro…
ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems
Eduardo Zimelewicz, Marcos Kalinowski, Daniel Mendez +13
[Context] Systems incorporating Machine Learning (ML) models, often called ML-enabled systems, have become commonplace. However, empirical evidence on how ML-enabled systems are en…
A Multivocal Literature Review on the Benefits and Limitations of Automated Machine Learning Tools
Kelly Azevedo, Luigi Quaranta, Fabio Calefato +1
Context. Advancements in Machine Learning (ML) are revolutionizing every application domain, driving unprecedented transformations and fostering innovation. However, despite these…