This is exactly my experience and that of most people in the field I've worked with. In fact, many people have said the quality/processing of data is much more important than the machine learning model you use.
The exception is those fields that have physical data, like computer vision or speech recognition. In those fields, the actual model matters a lot more.
I think every job has this tendency, where the public focus on the most exciting and interesting part, and ignore the mundane but also extremely important parts.
And on the matter of skill/ability, in spite of not being a Kaggle winner, the author couldn't do their job without a good understanding of machine learning models. To do machine learning in practice, you must know both the software development/systems side and the maths/stats/ML side.
The exception is those fields that have physical data, like computer vision or speech recognition. In those fields, the actual model matters a lot more.
I think every job has this tendency, where the public focus on the most exciting and interesting part, and ignore the mundane but also extremely important parts.
And on the matter of skill/ability, in spite of not being a Kaggle winner, the author couldn't do their job without a good understanding of machine learning models. To do machine learning in practice, you must know both the software development/systems side and the maths/stats/ML side.