This paper addresses AI product prototy**, focusing on the challenges posed by the
probabilistic nature of AI behavior and the limited accessibility of prototy** tools to AI non …

Within the healthcare sector, deploying Machine Learning (ML) models involves trial-and-
error approaches, considerable time to create task-specific models, and collaboration …

Deep Learning models have become an integrated component of modern software systems.
In response to the challenge of model design, researchers proposed Automated Machine …

Traditional Software Reliability Growth Models (SRGMs) depend on unrealistic assumptions,
which makes it difficult to capture the complexities of modern software development. Recent …

Cross-project defect prediction (CPDP) leverages machine learning (ML) techniques to
proactively identify software defects, especially where project-specific data is scarce …

The growing demand for data-driven applications requires AI solutions that are both efficient
and scalable to support big data analytics across diverse industries. Large Language …

Context: In large-scale software development, a large number of bug reports are submitted
during software development and maintenance. Practitioners need the ability to analyze this …

Gabriel de Araujo Carvalho Assessing the benefits of MLOps for Supervised Online Regression
Machine Learning Page 1 Gabriel de Araujo Carvalho Assessing the benefits of MLOps for …