Toward Model Selection Through Measuring Dataset Similarity on TensorFlow Hub

For novice developers, it is a challenge to select the most suitable model without prior knowledge of artificial intelligence (AI) development. The main objective of our system is to provide an automated approach to present models through dataset similarity. We present a system that enables novel developers to select the best model among existing models from an online community of TensorFlow Hub (TF Hub). Our strategy was to use the similarity of two datasets as a measure to determine the best model. By conducting a systematic review, we identified multiple limitations, each of which corresponds to a function to be implemented in our proposed system. We then created a model selection system that enables novice developers to select the most suitable ML model without prior knowledge of AI by implementing the identified functions. The analysis of this study reveals that our proposed system performed better by successfully addressing three out of six identified limitations.