ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913193552510976 |
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| author | Abdelkader, Hala Abdelrazek, Mohamed Barnett, Scott Schneider, Jean-Guy Rani, Priya Vasa, Rajesh |
| author_facet | Abdelkader, Hala Abdelrazek, Mohamed Barnett, Scott Schneider, Jean-Guy Rani, Priya Vasa, Rajesh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06513 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study Abdelkader, Hala Abdelrazek, Mohamed Barnett, Scott Schneider, Jean-Guy Rani, Priya Vasa, Rajesh Software Engineering Artificial Intelligence Machine Learning 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. |
| title | ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2401.06513 |