工程实践

模型工程、评测流程与上线实践。

arXiv cs.LG69

Forward-Deployed Full-Stack Engineering for Autonomous Cloud MLOps

arXiv:2608.29615v2 Announce Type: replace-cross Abstract: Across industries, machine-learning systems support applications ranging from prediction and anomaly detection to forecasting, optimization, and scheduling, yet operationalizing these systems requires coordinating application development, model pipelines, cloud infrastructure, security, deployment, monitoring, retraining, recovery, and rollback. We present an evidence-gated multi-agent framework for transforming a natural-language MLOps cloud engineering task into a verified repository and operational cloud deployment. The framework combines graph engineering, loop engineering, and agent harness engineering. A stateful Graph Orchestrator coordinates specialized agents for repository generation, review, execution, verification, release, and monitoring while governing workflow dependencies, evidence gates, retry bounds, recovery paths, and termination. Consequential lifecycle transitions proceed only when their required predicates are supported by verifiable execution or runtime evidence. Verification failures activate bounded reflection, repair, and re-verification, while runtime evidence of failure, drift, degradation, or policy violation can trigger bounded adaptation, recovery, or rollback. Agent harness engineering constrains repository generation, review, and repair, artifact execution, and cloud operations through controlled capabilities and isolated execution environments. We realize the framework on Google Cloud Platform and evaluate repository completeness, controlled execution, evidence-gated transitions, cloud promotion, and bounded recovery. Our experimental results show that the framework prevents unsupported lifecycle transitions and drives each run toward either a verified operational deployment or an auditable terminal failure.

arXiv cs.AI69

Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics

The rapid evolution of Intelligent Transportation Systems and Logistics (ITS\&L) has become a cornerstone of the modern social economy, relying heavily on the integration of Data, Artificial Intelligence (AI), and, more specifically, Machine Learning (ML). This paper provides a comprehensive review of Trustworthy Data and Machine Learning Operations (DataOps and MLOps) in the ITS\&L domain, underscoring their importance in improving efficiency, reliability, and decision-making precision within transportation and logistics services. We begin by identifying gaps in current literature, offering clear context for our contribution. Subsequently, we explore the complexities of DataOps and MLOps, discussing their necessity, key components, available tools, practical insights, and case studies relevant to ITS\&L. Additionally, we address the critical issue of Trustworthiness in AI applications, examining methods and tools designed to strengthen confidence in AI systems - especially in real-world ITS\&L scenarios. The paper concludes with a discussion of persisting challenges and future prospects in this rapidly advancing field, aiming to serve as a vital resource for researchers, industry practitioners, and policy makers. Overall, this work not only establishes a foundational understanding of DataOps and MLOps in ITS\&L but also charts a path for further research and innovation in developing more efficient, sustainable, and trustworthy intelligent transportation and logistics systems.

arXiv cs.AI72

面向自主云 MLOps 的前向部署式全栈工程

跨越多个行业，机器学习系统支撑着从预测和异常检测到预测、优化和调度等一系列应用，然而，要将这些系统投入实际运行，需要协调应用开发、模型流水线、云基础设施、安全性、部署、监控、重新训练、恢复和回滚。我们提出一种证据门控的多智能体框架，用于将自然语言 MLOps 云工程任务转换为经验证的代码仓库和可运行的云端部署。该框架结合了图工程、循环工程和智能体支架工程。一个有状态的 Graph Orchestrator 协调专门的智能体，负责代码仓库生成、审查、执行、验证、发布和监控，同时管理工作流依赖、证据门控、重试上限、恢复路径和终止。只有当所需谓词得到可验证的执行证据或运行时证据支持时，相关的生命周期转换才会进行。验证失败会触发有界的反思、修复和重新验证；而失败、漂移、退化或策略违规的运行时证据则可触发有界的自适应、恢复或回滚。智能体支架工程通过受控能力和隔离执行环境，约束代码仓库生成、审查与修复、制品执行以及云操作。我们在 Google Cloud Platform 上实现了该框架，并评估了代码仓库完整性、受控执行、证据门控转换、云端上线以及有界恢复。实验结果表明，该框架可防止缺乏支持的生命周期转换，并推动每次运行要么走向经验证的可运行部署，要么走向可审计的终止性失败。