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条目 E-NF2:MLOps 架构指南 — 25 条模型集成/部署规范(灰色文献综述)
arXiv:2606.06535 工程化 综述 OA · 绿色 被引 0 · S2 + OpenAlex

本文贡献了一组在架构层面具有重要意义的 MLOps 集成与部署指南(共 25 条),分为五类,并阐述其对整体系统架构的影响。This work contributes a collection of 25 architecturally significant MLOps guidelines for model integration and deployment, organized into five categories, and describes their impact on the overall system architecture.

⑤ MLOps系统综述(arXiv:2604.16371)
⑤ MLOps系统综述(arXiv:2604.16371)
arXiv:2604.16371 工程化 综述 OA · 绿色 被引 2 · S2

对聚焦MLOps工具的学术文献进行系统综述,揭示其功能、范围及其旨在解决的挑战,并突出真实MLOps pipeline中各工具间互操作性的重要性。A systematic review of the academic literature focused on MLOps tools is conducted to reveal their function, scope, and the challenges they are designed to address and highlight the importance of interoperability across MLOps tools in real-world MLOps pipelines.

Generalizing from a Few Examples: A Survey on Few-Shot Learning
基于少量样本的泛化:小样本学习综述
arXiv:1904.05046 工程化 综述 OA · 绿色 被引 2185 · S2

一篇全面综述,旨在深入理解 Few-shot Learning,并从三个维度对 FSL 方法进行分类:数据层面——利用先验知识扩充监督经验;模型层面——利用先验知识缩小假设空间规模;算法层面——利用先验知识改变在给定假设空间中对最优假设的搜索方式A thorough survey to fully understand Few-shot Learning and categorize FSL methods from three perspectives: data, which uses prior knowledge to augment the supervised experience; model, which uses prior knowledge to reduce the size of the hypothesis space; and algorithm, which uses prior knowledge to alter the search for the best hypothesis in the given hypothesis space.