• This need gap seems to be hard to acknowledge by those who have easy/cheaper access to hardware, electricity, in general better infrastructure and so I felt the responsibility to give a better context.

    There are reputed, fully meritorious govt. run Engineering colleges in India, where tuition fee is ~100 USD/Year. Many of the students studying there are in poverty, govt. provides free laptop albeit obviously cheap one. Almost everyone studying there are placed in top companies around the world.

    In my previous startup I've conducted ~80 ML/DS interviews, fresh graduates from above colleges perform very well in DSA & other CS aspects; but perform poor with ML when compared to those from expensive private institutions.

    When I enquired them, it came down to lack of proper access to ML hardware. Their college labs are not equipped to provide ML training at scale, their access to Internet is limited to make use of Google Colaboratory or similar services.

    Yes, they could run a CPU bound training for days, but it isn't practical and many don't have consistent power supply.

    Economic disadvantage in education is real, more pronounced when it comes to ML in my experience.