Four "R"s to Machine Learning Software Development
Having developed and maintained data-driven products based on ML models in production -- across companies of all shapes and sizes, including internationally (!), I've found that there are 4 themes to ML software development.
How robust is the data processing? How high do you score on the ML Test Score (Google, 2017)?
Design: Do you have an architecture diagram? Have you defined an input and output spec?
Is there a testing suite?
Do you use version control?
Do you connect to the data source(s) directly?
(Python) Do you follow PEP 8 guidelines? And a style guide?
Does the code need refactoring? Or is it (relatively) easy to understand what the code is doing and how to modify it?
Is there logging?
Should I not ask about documentation? :)
Keywords: Data products, Machine Learning software development
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