Robustness Attributes to Safeguard Machine Learning Models in Production

Machine learning (ML) has revolutionized various industries by enabling the development of complex models that learn from data and make accurate predictions. However, moving from prototyping ML models to production software systems poses robustness challenges due to the lack of standardization around ML tools and processes. To tackle these challenges in deployed ML models, different elements of robustness must be addressed, including transparency, safety, and security along with adopting a unified language and terminologies. This paper aims to highlight the key robustness challenges that ML models encounter when deployed in production environments, and to emphasize the significance of proactively tackling these challenges. We also introduce various patterns for safeguarding ML models that need to be implemented on both the ML models and the software system sides.