ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
This addresses the problem of ML model robustness and trustworthiness for software engineers and ML providers in production environments, but it appears incremental as it builds on existing deployment concerns.
The paper tackles the challenge of ensuring safety, security, and transparency when deploying machine learning models from prototyping to production in software systems, introducing the ML-On-Rails protocol and evaluating it in a real-world case study of the MoveReminder application.
Machine learning (ML), especially with the emergence of large language models (LLMs), has significantly transformed various industries. However, the transition from ML model prototyping to production use within software systems presents several challenges. These challenges primarily revolve around ensuring safety, security, and transparency, subsequently influencing the overall robustness and trustworthiness of ML models. In this paper, we introduce ML-On-Rails, a protocol designed to safeguard ML models, establish a well-defined endpoint interface for different ML tasks, and clear communication between ML providers and ML consumers (software engineers). ML-On-Rails enhances the robustness of ML models via incorporating detection capabilities to identify unique challenges specific to production ML. We evaluated the ML-On-Rails protocol through a real-world case study of the MoveReminder application. Through this evaluation, we emphasize the importance of safeguarding ML models in production.