  Increasing availability of machine learning (ML) frameworks and tools, as
well as their promise to improve solutions to data-driven decision problems,
has resulted in popularity of using ML techniques in software systems. However,
end-to-end development of ML-enabled systems, as well as their seamless
deployment and operations, remain a challenge. One reason is that development
and deployment of ML-enabled systems involves three distinct workflows,
perspectives, and roles, which include data science, software engineering, and
operations. These three distinct perspectives, when misaligned due to incorrect
assumptions, cause ML mismatches which can result in failed systems. We
conducted an interview and survey study where we collected and validated common
types of mismatches that occur in end-to-end development of ML-enabled systems.
Our analysis shows that how each role prioritizes the importance of relevant
mismatches varies, potentially contributing to these mismatched assumptions. In
addition, the mismatch categories we identified can be specified as machine
readable descriptors contributing to improved ML-enabled system development. In
this paper, we report our findings and their implications for improving
end-to-end ML-enabled system development.
