Collaboration on complex development projects almost always presents challenges. For traditional software projects, these challenges are well known, and over the years a number of approaches to addressing them have evolved. But as machine learning (ML) becomes an essential component of more and more systems, it poses a new set of challenges to development teams. Chief among these challenges is getting data scientists (who employ an experimental approach to system model development) and software developers (who rely on the discipline imposed by software engineering principles) to work harmoniously.
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