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Deep learning (DL) is revolutionizing many fields. However, there is a major bottleneck for the wide adoption of DL: the pain of model selection , which requires exploring a large config space of model architecture and training hyper-parameters before picking the best model. The two existing popular paradigms for exploring this config space pose a false dichotomy. AutoML-based model selection explores configs with high-throughput but uses human intuition minimally. Alternatively, interactive human-in-the-loop model selection completely relies on human intuition to explore the config space but often has very low throughput. To mitigate the above drawbacks, we propose a new paradigm for model selection that we call intermittent human-in-the-loop model selection . In this demonstration, we will showcase our approach using five real-world DL model selection workloads. A short video of our demonstration can be found here: https://youtu.be/K3THQy5McXc.