Name
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 product 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-58666-9_2 | BPM |
DocType | Citations | PageRank |
Conference | 3 | 0.38 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rama Akkiraju | 1 | 571 | 49.91 |
| Vibha Singhal Sinha | 2 | 189 | 15.92 |
| Anbang Xu | 3 | 351 | 30.52 |
| Jalal Mahmud | 4 | 684 | 51.97 |
| Pritam Gundecha | 5 | 81 | 6.41 |
| Zhe Liu | 6 | 18 | 3.39 |
| Xiaotong Liu | 7 | 4 | 1.47 |
| John Schumacher | 8 | 3 | 0.38 |