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Machine learning (ML), especially with the emergence of large language models(LLMs), has significantly transformed various industries. However, thetransition from ML model prototyping to production use within software systemspresents several challenges. These challenges primarily revolve around ensuringsafety, security, and transparency, subsequently influencing the overallrobustness and trustworthiness of ML models. In this paper, we introduceML-On-Rails, a protocol designed to safeguard ML models, establish awell-defined endpoint interface for different ML tasks, and clear communicationbetween ML providers and ML consumers (software engineers). ML-On-Railsenhances the robustness of ML models via incorporating detection capabilitiesto identify unique challenges specific to production ML. We evaluated theML-On-Rails protocol through a real-world case study of the MoveReminderapplication. Through this evaluation, we emphasize the importance ofsafeguarding ML models in production.

ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study