  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.
