  Deep learning has become the most popular direction in machine learning and
artificial intelligence. However, the preparation of training data, as well as
model training, are often time-consuming and become the bottleneck of the
end-to-end machine learning lifecycle. Reusing models for inferring a dataset
can avoid the costs of retraining. However, when there are multiple candidate
models, it is challenging to discover the right model for reuse. Although there
exist a number of model sharing platforms such as ModelDB, TensorFlow Hub,
PyTorch Hub, and DLHub, most of these systems require model uploaders to
manually specify the details of each model and model downloaders to screen
keyword search results for selecting a model. We are lacking a highly
productive model search tool that selects models for deployment without the
need for any manual inspection and/or labeled data from the target domain. This
paper proposes multiple model search strategies including various
similarity-based approaches and non-similarity-based approaches. We design,
implement, and evaluate these approaches on multiple model inference scenarios,
including activity recognition, image recognition, text classification, natural
language processing, and entity matching. The experimental evaluation showed
that our proposed asymmetric similarity-based measurement, adaptivity,
outperformed symmetric similarity-based measurements and non-similarity-based
measurements in most of the workloads.
