No abstract

In recent years, a number of business reasons have caused software development to become increasingly distributed. Remote development of software offers several advantages, but it is also fraught with challenges. In this paper, we report on our study of distributed software development that helped shape a research agenda for this field. Our study has identified four areas where important research questions need to be addressed to make distributed development more effective. These areas are: collaborative software tools, knowledge acquisition and management, testing in a distributed set-up and process and metrics issues. We present a brief summary of related research in each of these areas, and also outline open research issues.

No abstract

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, 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 authors' personal experience of building large scale real-world machine learning models to help organizations achieve higher levels of maturity independent of their starting point.

No abstract