用数据湖思路统一管理模型、代码和数据，提升可复用性与审计能力。
Model Lake: a New Alternative for Machine Learning Models Management and Governance
- 构建集中式模型湖框架，整合数据、代码与模型资产
- 实现模型全生命周期管理，支持版本追踪与审计
- 适合需要规范模型治理的科研或企业团队
人工智能与数据科学在各行业的兴起凸显了机器学习（ML）模型管理与治理的迫切需求。传统方法常依赖分散的存储系统，缺乏版本控制、审计与复用的标准流程。受数据湖理念启发，本文提出机器学习模型湖（Model Lake）概念，作为组织内数据、代码与模型的集中化管理框架。文中深入探讨了模型湖的架构基础、核心组件、运营优势与实际挑战。研究指出，采用模型湖可显著提升模型全生命周期管理、发现能力、审计效率与复用率。此外，本文通过真实应用案例展示了其在数据、代码与模型管理上的变革性影响。
原文摘要 · Abstract (English)
The rise of artificial intelligence and data science across industries underscores the pressing need for effective management and governance of machine learning (ML) models. Traditional approaches to ML models management often involve disparate storage systems and lack standardized methodologies for versioning, audit, and re-use. Inspired by data lake concepts, this paper develops the concept of ML Model Lake as a centralized management framework for datasets, codes, and models within organizations environments. We provide an in-depth exploration of the Model Lake concept, delineating its architectural foundations, key components, operational benefits, and practical challenges. We discuss the transformative potential of adopting a Model Lake approach, such as enhanced model lifecycle management, discovery, audit, and reusability. Furthermore, we illustrate a real-world application of Model Lake and its transformative impact on data, code and model management practices.
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