Characterizing machine learning process: A maturity framework
This addresses the problem of making ML models work effectively for enterprises, though it is incremental as it adapts an existing software framework to the ML domain.
The paper tackles the lack of enterprise-focused machine learning processes by proposing a maturity framework for ML model lifecycle management, based on a reinterpretation of the software Capability Maturity Model and best practices from real-world experience.
Academic literature on machine learning modeling fails to address how to make machine learning models work for enterprises. For example, existing machine learning processes cannot address how to define business use cases for an AI application, how to convert business requirements from offering managers into data requirements for data scientists, and how to continuously improve AI applications in term of accuracy and fairness, and how to customize general purpose machine learning models with industry, domain, and use case specific data to make them more accurate for specific situations etc. Making AI work for enterprises requires special considerations, tools, methods and processes. In this paper we present a maturity framework for machine learning model lifecycle management for enterprises. Our framework is a re-interpretation of the software Capability Maturity Model (CMM) for machine learning model development process. We present a set of best practices from our personal experience of building large scale real-world machine learning models to help organizations achieve higher levels of maturity independent of their starting point.