Greetings all,

I’ve recently been recommended to read Hidden Technical Debt in Machine Learning Systems, and have found its insights valuable. Knowing that this will benefit at least one upcoming project I am working, what other essential reading is out there for taking machine learning projects to production in a principled manner? Whose other mistakes can I learn from before making my own?