  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.
