I think the article is actually saying the exact opposite - that Tensorflow / PyTorch / sklearn code soup from “trade school” sources like bootcamps or quick on

I think the article is actually saying the exact opposite - that Tensorflow / PyTorch / sklearn code soup from “trade school” sources like bootcamps or quick online programs are not very valuable out in the world. You might be misunderstanding the focus on data cleaning and feature engineering as being less specialized than say PyTorch coding but it’s exactly the opposite. The most critical aspects of ML engineering for production are all about advanced statistics. Understanding multicollinearity, overfitting, dimensionality reduction, convergence, and time series issues like assumptions of stationarity or conditional independence effects. Any engineer can crank out neural network software - that has pretty much zero value. Value lies in realizing some stratification error in the data and following that lead to use a multi-level model to control for it. Value lies in realizing several key feature inputs are correlated on a seasonal basis - leading to multicollinearity - and then setting up some adaptive feature aggregation to mitigate it and dashboards with things like variance inflation factor to be able to raise alerts on it across time. Value lies in working on small data problems and using literature review to determine the best prior to use for a Bayesian model, and doing robust posterior predictive checks to validate it. These things require many years of education and experience dealing with statistical irregularities, understanding confounders and causal inference, understanding missing data treatments, understanding time series forecasting. You cannot learn that in 101 courses that overly focus on the mechanics of how to type Tensorflow or sklearn code - that part can be picked up by anyone in a month or two. And mere intro to data cleaning and plotting distributions or proportions of missing data is not a substitute for actual statistical knowledge.