arXiv:2606. 06535v1 Announce Type: cross Abstract: Context.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
The paper investigates how software engineering researchers approach machine learning in their work, reviewing research, review, and education practices. It finds that while many researchers follow data collection, model training, and evaluation routines, only a minority adopt recommended practices such as hyperparameter tuning. Common challenges include data handling, evaluating non‑functional properties, and integrating human expertise, and education often relies on hands‑on activities alongside traditional methods.
The paper introduces a multi‑agent framework that transforms natural‑language MLOps tasks into verified repositories and operational cloud deployments. It uses a stateful Graph Orchestrator to coordinate agents for repository generation, review, execution, verification, release, and monitoring, ensuring lifecycle transitions only occur when supported by verifiable evidence. The framework, implemented on Google Cloud Platform, demonstrates prevention of unsupported transitions and drives each run toward a verified deployment or an auditable failure.
arXiv:2606. 17915v1 Announce Type: cross Abstract: Big-Data-as-a-Service (BDaaS) platforms require re liable automation across data ingestion, cleaning, feature engi neering, model development, deployment, and post-deployment monitoring.
arXiv:2602.24055v5 Announce Type: replace Abstract: This study proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI syste...
arXiv:2604. 10311v2 Announce Type: replace Abstract: Artificial Intelligence (AI) models, encompassing both traditional machine learning (ML) and more advanced approaches such as deep learning and large language models (LLMs), play a central role in modern applications.
The paper reviews Trustworthy Data and Machine Learning Operations (DataOps and MLOps) for Intelligent Transportation Systems and Logistics (ITS&L). It identifies gaps in current literature, discusses the complexities, key components, tools, practical insights, and case studies relevant to ITS&L, and examines methods to strengthen trustworthiness in AI applications. The authors conclude by outlining ongoing challenges and future prospects, positioning the work as a resource for researchers, industry practitioners, and policymakers.