* If they already have a mac, fine, but would not recommend buying a new one just for ML. Homebrew is not fun, docker on mac is not fun (better than before though), compilers / Xcode setup are not fun, spotty M1 support is not fun. CUDA is not fun either (but its no fun anywhere tbh).
* Chromebooks come with a Debian linux container out of the box, it's solid.
* AWS offers cloud credit for learners and startups, a good deal but don't get locked in. That's why I recommend learning the building blocks (eg virtual servers, storage, notebooks are essentially commoditized) and not the higher-level and more expensive products like sagemaker deployment apis that are tightly integrated into Amazon specifically.
* And my usual reminder, don't do personal stuff on your work machine, and don't do work stuff on your personal machine.
I haven't really looked at Chromebooks in awhile. They were like glorified calculators years ago and I never took them seriously. I've heard mixed things about the M1 chips but haven't upgraded my machine yet so don't have hands on personal experience.
I agree on the sagemaker integration thing, especially with respect to long term lock in. They do make it really easy to get moving on a data project in AWS without having to wrestle with EC2. I've run into a lot of people who just need something that works quickly and smoothly so it's a good option for that (probably why Amazon invested so heavily in marketing it as such).
Your usual reminder is great. I mentor some students in an ML club at ASU who probably need to hear that a few times before they start their first jobs...
Good advice. I would add:
* If they already have a mac, fine, but would not recommend buying a new one just for ML. Homebrew is not fun, docker on mac is not fun (better than before though), compilers / Xcode setup are not fun, spotty M1 support is not fun. CUDA is not fun either (but its no fun anywhere tbh).
* Chromebooks come with a Debian linux container out of the box, it's solid.
* AWS offers cloud credit for learners and startups, a good deal but don't get locked in. That's why I recommend learning the building blocks (eg virtual servers, storage, notebooks are essentially commoditized) and not the higher-level and more expensive products like sagemaker deployment apis that are tightly integrated into Amazon specifically.
* And my usual reminder, don't do personal stuff on your work machine, and don't do work stuff on your personal machine.
I haven't really looked at Chromebooks in awhile. They were like glorified calculators years ago and I never took them seriously. I've heard mixed things about the M1 chips but haven't upgraded my machine yet so don't have hands on personal experience.
I agree on the sagemaker integration thing, especially with respect to long term lock in. They do make it really easy to get moving on a data project in AWS without having to wrestle with EC2. I've run into a lot of people who just need something that works quickly and smoothly so it's a good option for that (probably why Amazon invested so heavily in marketing it as such).
Your usual reminder is great. I mentor some students in an ML club at ASU who probably need to hear that a few times before they start their first jobs...