So, your manager says “the data scientists demoed this. Put it into production”. Do you
a) pick an appropriate tool and rewrite their code.
b) pick an appropriate tool (python type annotations, this verifier), use it to make the python code robust, and deploy the python code in production?
Even if doing a) is as easy as doing b) and produces faster, more efficient code, doing a) again will be more expensive than doing b) again when the data scientists update their python code (even if the typed python and the untyped python are in separate repositories, but ideally, the data scientists would continue working with the ‘solidified’ code, adding duct-typed code at will)
a) pick an appropriate tool and rewrite their code.
b) pick an appropriate tool (python type annotations, this verifier), use it to make the python code robust, and deploy the python code in production?
Even if doing a) is as easy as doing b) and produces faster, more efficient code, doing a) again will be more expensive than doing b) again when the data scientists update their python code (even if the typed python and the untyped python are in separate repositories, but ideally, the data scientists would continue working with the ‘solidified’ code, adding duct-typed code at will)