A more or less new trend which I observe is integration of data science processes (i.e. workflows required to setup and execute experiments) to design and delivery of user facing apps.
So I assume business expects this new data science capability to introduce some new features either competitive advantages as a new product element.
"Companies approach machine learning, and deep learning, as if they are doing a software engineering project, with waterfall processes and gantt charts, thinking that they can somehow totally avoid the very difficult work necessary to design a complex and highly accurate machine learning solution." (Charles Martin)
Agile seems be of neither help:
"The traditional agile/scrum methodology combined with JIRA may not address the nature of it being a series of hypotheses driven experiments. Time estimates do vary over different projects based on nature of business questions asked. Perhaps, a case-specific discussion along with Kanban style milestone based progress estimation may help." (Kirti Chawla)
P.S. yet a quite clumsy formulation from a recent LinkedIN discussion, please if possible leave a comment to help get this straight (if possible) before just downvoting. Thanks.
So does Kanban help here? Or what is other proven process model? There is also a DevOps approach in this context, but this might be considered as "too technical/low-level" from PM perspective I assume.