The use of machine learning (ML) could improve many business functions and meet many needs for organizations. For example, ML capabilities can be used to suggest products to users based on purchase history; provide image recognition for video surveillance; identify spam email messages; and predict courses of action, routes, or diseases, among others. However, in most organizations today (with the exception of large high-tech companies, such as Google and Microsoft), development of ML capabilities is still mainly a research activity or a standalone project, and there is a dearth of existing guidance to help organizations develop these capabilities.
Integrating ML components into applications is limited by the fragility of these components and their algorithms. They are susceptible to changes in data that could cause their predictions to change over time. They are also limited by mismatches between different system components.
For example, if an ML model is trained on data that is different from data in the operational environment, field performance of the ML component will be dramatically reduced. In this blog post, which is adapted from a paper that SEI colleagues and I presented at the 2019 Association for the Advancement of Artificial Intelligence (AAAI) fall seminar (Artificial Intelligence in Government and Public Sector), I describe how the SEI is developing new ways to detect and prevent mismatches in ML-enabled systems so that ML can be adopted with greater success to drive organizational improvement.