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Discussion on: What I learned in 6 months at an AI company

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zmarkan profile image
Zan Markan Author

That's a great question!

On one hand using a common language for multiple tasks is very appealing - look at the JS ecosystem with browser/Node (and now Deno), and other newer efforts like Kotlin for multiplatform.

In the short term, I think it will be super difficult to unseat Python for data science, given how prevalent it seems to be. I've heard people say good things about Julia as a superior computational language but not sure where it is going.
Developers will be quicker to change and are more likely to diverge into other areas.

In the long term, we're moving towards greater abstraction everywhere (both data science and dev) - and with this there'll be more emphasis on specific and specialised tools, glued together with scripting. (At least for the most common use-cases)
The programming language itself will become less relevant as it becomes just a glue technology.

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jonathanpwheat profile image
Jonathan Wheat

We're kind of split language I guess.

Developers here can help prototype things out in python if they have an understanding of the data and are working closely with a data scientist on a problem. I think all of our data scientists are also C/C++ guys, and rewrite and compile things down for production for speed and performance gains.

Do you use python on production? If so, do you see any hits on speed / performance?