[chalst]

> for niches such as ML and data science, I find you just can't beat the Python ecosystem

There definitely are many areas where Python is best, but for ML and data science, Julia is (i) very competitive in library coverage, (ii) more performant and flexible, and (iii) has a very good Python bridge if it's needed.

I can imagine there are niches within ML and data science where what you need are Python-only libraries, you don't miss anything restricting yourself to the numpy type hierarchy and there's no advantage to calling the libraries from Julia, but I'm curious to check if that is what you actually meant and if so, what you are doing.

[SatvikBeri]

After using python for data science for 3 years (since founding the current startup), I mostly switched to Julia about 4 months ago. So far the only Python libraries I've really missed are boto3 and sqlalchemy for sql generation – both of which can easily be called in Julia using PyCall.

I think people often underestimate just how much faster Julia is than numpy, I've consistently seen performance improvements on the order of 10x-30x when porting code.