[___luigi]

ML can help reduce technical debt at logic layer, but it increases the technical debt at the infrastructure layer. It's a challenge for any company to deploy, manage and monitor models in production. If you can get away with a simple rule, that's a bigger win for the product (I'm not talking about research here).

In the community, there is a trend that "complicated == better". imho, more is less in industrial ML. You need to deal with model management, worry about inference & latency when the model gets bigger. The author has another article where he argues that data scientists need to be full stack ninja. While I don't fully agree with that statement, I think it benefits the company in many many ways. Data scientists need to meet engineers in the middle, and all these challenges need to be considered from day 1. Another trend I see is that some data scientists are not driven by the question "Can we solve this problem for the company?", but rather "Can we solve this problem using ML/DL?". This will lead data scientists to use the shiny and trendy models, even if it is not suitable for the job. I would blame management here, in some environments, data scientists are evaluated based on "fancy" models they build, not solutions that they provide. Solutions can be simple (but not simpler) rules.