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> I can give you one reason: Most of the ML tutorials online work on toy problems. Full stop.

And most libraries and implementations are also in Python because they work on toy problems?

> There's a reason why data engineers are more in demand now than data scientists. With Scikit learn, keras etc it's easy to build models. It's not easy to deploy models to production. Half the tutorials don't teach the important bits: that your model needs to live in production.

There is also a reason why you employ full stack web developer instead of frontend developer. I can tell you what is the reason - it's cheaper than employing frontend developer and backend developer. And for toy problems you can hire fullstack developer.

> Now, if you write your programs using Gonum or Gorgonia, you need to think a lot deeper about what your model is doing, about memory about things that software engineers think about. It's not easier, but it's the only sustainable way forwards.

So you are implying that whole industry, researchers and ML practitioners got that wrong and they should use Go now? I know a lot of people working on ML related problems and none of them use Go for their ML work. Some of them have Go in their stacks, sure, but it's not used for ML directly. And they solve practical business problems.

I've also worked before with two ML researchers respected in the industry and I can assure you, they are not working on toy problems and they do not know Go. And this is coming from Go user and enthusiast. Programming languages are just tools, not a religion.

Perhaps "toy problem" is the wrong term to use. "Deploy unfriendly" is perhaps the term I should have used.

Programming languages are tools indeed. Some tools make life easy in one way, some tools make life easy in other ways.

> Some tools make life easy in one way, some tools make life easy in other ways.

Exactly, you always choose between different set of trade-offs.

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