More specifically, at least in industry, we need SRE / ops support to mature. Taking a team of people who are highly specialized at the research layer of a statistical computing problem, then treating them like they are immature when they get massively overloaded also solving credential management, Kubernetes config, web service hardening, efficient data pipelining, etc. etc. is just such a whiny and immature thing to see come out of infra / ops team leaders, that leads to burning out ML engineers, and wasting a lot of money failing to extract value from their comparative advantage for the business just because infra / ops leaders can’t get it together and solve ML coordination problems.

Right. I think it ignores the importance of the data when building a model. Even great data can lead to difficult modeling scenarios. The premise that a “solution” can guarantee (or even partially guarantee) to be useful is misleading.

I don't think the author would disagree with you. In fact, I think this article was highlighting one specific area in which the field could improve.