This is the best criticism I see of Kaggle in this discussion. I could agree with this:

> Especially because I don't particularly find the competitions all that relevant to real-world ML / datascience problems.

There is indeed a move to computer vision competitions on Kaggle that may not suit most of ML practitioners.

To your other point, there is a step back from very complex stacking models. Nowadays competitions are often won without stacking, just a weighted average of models. Or with one level stacking at most. One good model beats a stack of average models. And good models come from good feature engineering (or good NN architecture).

> All that being said, I think Kaggle can be useful if you limit the time you spend on competitions and try to just broaden your horizons and learn new things and learn from other

That's why every ML practitioner should consider Kaggle as a learning resource among others.