drowning_sushi

This is very subjective. Using Python influences your architecture in ways you would not encounter with other languages.

I maintain a critical service written in Python and hosted in AWS and with about 40 containers it can do 1K requests/sec with good reliability. But we see issues with http libraries and systemic pressure within the service.

50-75% of the employees at MS HQ regularly work out of the office. (Source: I live close by).

This mandate is not at all surprising given MS invested heavily in new, revamped offices, which they had started before the pandemic. How did folks who relocated to other areas not see this coming.

We did not have a product manager. In my team, there was frequent churn at the manager position. Which should have been a clear indicator that my division had no clue what they were trying to do.

I was naive and trying too hard to stick to ML but lesson learnt eventually.

I too moved away from ML after actively pursuing it for many years. YMMV but here are my reasons

- Scientists dont always make the best 'clients'. The requirements you spend months implementing may be completely obsolete by the time you are done and then completely unused. - You often dont understand or are made aware of the impact of your work. - Its challenging to compete with Masters/Phd graduates who have spent years delving into ML. Entry-level knowledge only takes you so far. So its more likely that you wont work on cutting edge ML research. - MLE work in my experience has been mostly around infrastructure management and data security. Again it has interesting challenges and hard problems to solve but with the speed of the AI world, it all boils down to facilitating the scientists and researchers as much as you can

Completely agree. Wordle is fun without the hyper-analysis