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.

翻译：人工智能与数据科学在各行业的兴起，突显了对机器学习模型进行有效管理与治理的迫切需求。传统的机器学习模型管理方法通常涉及分散的存储系统，且缺乏版本控制、审计与复用的标准化方法。受数据湖概念的启发，本文提出了机器学习模型湖的概念，作为组织内部数据集、代码与模型的集中式管理框架。我们深入探讨了模型湖的概念，阐述了其架构基础、关键组件、操作优势及实际挑战。我们讨论了采用模型湖方法的变革潜力，例如增强的模型生命周期管理、发现、审计与可复用性。此外，我们通过一个实际应用案例展示了模型湖在数据、代码与模型管理实践中的变革性影响。