4 Before you model: Communication and logistics of projects
This chapter covers
- Structuring planning meetings for ML project work
- Soliciting feedback from a cross-functional team to ensure project health
- Conducting research, experimentation, and prototyping to minimize risk
- Including business rules logic early in a project
- Using communication strategies to engage nontechnical team members
In my many years of working as a data scientist, I’ve found that one of the biggest challenges that DS teams face in getting their ideas and implementations to be used by a company is rooted in a failure to communicate effectively. This isn’t to say that we, as a profession, are bad at communicating.
It’s more that in order to be effective when dealing with ...
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