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That every programmer today can build and train an ML model is one of the biggest advancements of ML engineering in the past 10 years.

But as you say it's GIGO, the difficulty today is to know what to feed it and to know what that means for the real life performance. There are no great tools for that yet.

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> the difficulty today is to know what to feed it and to know what that means for the real life performance.

This has always been the difficulty.

Generalization is the fundamental problem in machine learning. Making easily available tools has led to an exponential growth in applications as more people play with it (many without understanding what they are doing or why), but predictably hasn't lead to an exponential growth in successful applications.

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