Terminology would probably be important – programming contests have a lot to do with programming, you literally sit and write programs that pass the test cases.

Terminology would probably be important – programming contests have a lot to do with programming, you literally sit and write programs that pass the test cases. I'm sure you meant it has fairly little to do with day-to-day programming for the majority of software engineers, which is correct. Regarding the article, I also disagree with it. I feel like the author is saying "algorithms != programming contests" which is false. Algorithms are the essence of programming contests, just like 'machine learning' is the essence of Kaggle competitions. Open up Bishop's "Pattern Recognition and Machine learning" and you won't find anything about "deploying your model to production", but you will find lots of math on how to build models because that is what ML is. What the author seems to be describing is data-science.