It's sad to see such "to hell with it" naivete (and the comments here with broad supporting evidence that data scientists are poor software developers. There a

It's sad to see such "to hell with it" naivete (and the comments here with broad supporting evidence that data scientists are poor software developers. There are also a lot of DS people wrestling with the deeper challenges at the heart of MLOps and beyond just advocating sane team-based workflows, are looking at ways to harden and structure ML specific processes in the planning, design, build, and deploy phases. Sean Taylor at Lyft on choosing metrics - https://medium.com/@seanjtaylor/designing-and-evaluating-metrics-5902ad6873bf https://medium.com/@seanjtaylor/designing-and-evaluating-met... Test coverage of ML models and a good discussion on why and how ML code testing isn't the same as traditional testing - https://www.jeremyjordan.me/testing-ml/ https://www.jeremyjordan.me/testing-ml/ A tool for Behavioral testing of NLP models - https://github.com/marcotcr/checklist https://github.com/marcotcr/checklist How to be a 10x data scientist (spoiler: have a professional end to end workflow) - https://towardsdatascience.com/how-to-be-a-10x-data-scientist-4718accf7d3f https://towardsdatascience.com/how-to-be-a-10x-data-scientis... Ml-specific technical debt, how to measure it, some ideas on how to handle it, this paper has spawned a lot of great tools - http://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf http://papers.nips.cc/paper/5656-hidden-technical-debt-in-ma... Adversarial instance and outlier detection - https://github.com/SeldonIO/alibi-detect https://github.com/SeldonIO/alibi-detect And of course a whole suite of tools and research beingn done on data, pipeline, and model management, optimizing deployment and serving, explainability and interpretability, all in the service of applying enterprise-ready, engineering-grade rigor to data science. And then you read something like this and want to tear your hair out.