  Transfer learning can be seen as a data- and compute-efficient alternative to
training models from scratch. The emergence of rich model repositories, such as
TensorFlow Hub, enables practitioners and researchers to unleash the potential
of these models across a wide range of downstream tasks. As these repositories
keep growing exponentially, efficiently selecting a good model for the task at
hand becomes paramount. By carefully comparing various selection and search
strategies, we realize that no single method outperforms the others, and hybrid
or mixed strategies can be beneficial. Therefore, we propose SHiFT, the first
downstream task-aware, flexible, and efficient model search engine for transfer
learning. These properties are enabled by a custom query language SHiFT-QL
together with a cost-based decision maker, which we empirically validate.
Motivated by the iterative nature of machine learning development, we further
support efficient incremental executions of our queries, which requires a
careful implementation when jointly used with our optimizations.
