Given a set of deep learning models, it can be hard to find models
appropriate to a task, understand the models, and characterize how models are
different one from another. Currently, practitioners rely on manually-written
documentation to understand and choose models. However, not all models have
complete and reliable documentation. As the number of machine learning models
increases, this issue of finding, differentiating, and understanding models is
becoming more crucial. Inspired from research on data lakes, we introduce and
define the concept of model lakes. We discuss fundamental research challenges
in the management of large models. And we discuss what principled data
management techniques can be brought to bear on the study of large model
management.

给定一组深度学习模型，找到适合的模型、理解模型、区分模型以及模型之间的差异是困难的。目前的从业者依赖手动编写的文档来理解和选择模型。然而，并非所有模型都有完整和可靠的文档。随着机器学习模型的增加，找到、区分和理解模型的问题变得更加关键。针对数据湖的研究启发，我们引入和定义了模型湖的概念。我们讨论了在大型模型管理中的基本研究挑战，并讨论了什么样的原则性数据管理技术可以应用于大型模型管理研究。