Flow with FlorDB：机器学习生命周期的增量上下文维护
Flow with FlorDB: Incremental Context Maintenance for the Machine Learning Lifecycle
AI总结：
本文提出了一种在不破坏敏捷开发的前提下，通过日志语句和后见日志从机器学习流水线中增量获取与查询元数据的技术，并在FlorDB系统中实现，有效解决了敏捷性与纪律之间的软件工程权衡问题。
摘要：
在本文中，我们提出了在不破坏敏捷实践的前提下，从机器学习流水线中增量获取和查询任意元数据的技术。我们将方法的核心放在开发者普遍使用的元数据生成技术——日志语句上，利用日志记录会创建上下文这一事实。我们展示了后见日志如何允许这些语句在事后被添加和执行，而无需开发者具备先见之明。可以对不完整元数据的关系视图进行查询，从而跨多个工作流版本按需动态地批量物化新元数据。这一过程以“元数据后置”的风格完成，脱离敏捷开发的关键路径。我们在名为FlorDB的系统中实现了这些想法，并展示了该数据上下文框架如何涵盖一系列临时元数据，以及当前由定制特征存储和模型仓库处理的特殊情况。通过一个包含机器学习和人类反馈的使用场景，我们阐明了这些组件技术如何结合起来，以解决敏捷性与纪律之间经典的软件工程权衡问题。
英文摘要：
In this paper we present techniques to incrementally harvest and query arbitrary metadata from machine learning pipelines, without disrupting agile practices. We center our approach on the developer-favored technique for generating metadata -- log statements -- leveraging the fact that logging creates context. We show how hindsight logging allows such statements to be added and executed post-hoc, without requiring developer foresight. Relational views of incomplete metadata can be queried to dynamically materialize new metadata in bulk and on demand across multiple versions of workflows. This is done in a "metadata later" style, off the critical path of agile development. We realize these ideas in a system called FlorDB and demonstrate how the data context framework covers a range of both ad-hoc metadata as well as special cases treated today by bespoke feature stores and model repositories. Through a usage scenario -- including both ML and human feedback -- we illustrate how the component techniques come together to resolve classic software engineering trade-offs between agility and discipline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
