What sorts of side projects would be good for ML infrastructure?
I appreciate the value of side projects mentioned on Taro, and I'd like give one a shot. Whenever I've looked before, I got caught up in not finding ones relevant to my work. I work on the backend side, working in the machine learning infrastructure/ops space. That's literally the example used in the side projects video of one where it's hard to do on your own... so is it still worth doing a side project? Maybe one in a similar language (C++), even if it's not related?
I feel like I'm just not being creative enough here, but I'd rather not do a side project that's super unrelated to my area (e.g. learning a ton of frontend to build an app that has a small machine learning component) unless it's still valuable. Maybe to make this more general, how did those of you working on the backend get started with side projects?
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Discussion
(2 comments)- 4 months agoRobinhood, Meta, Course Hero, PayPal
I Googled for "machine learning side projects" and found this article which seems pretty good.
Making a delightful, easy-to-use front-end is much harder than people think, so I agree that it's probably not best to pick it up just for a side project (unless you're considering a pivot into that area). The back-end projects I've seen be successful are generally REST APIs. To expose it, you can bundle whatever service you make with a Postman collection (just include it in the GitHub and add instructions on using it to the README).
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4 months agoEntry-Level Software Engineer [IC1] [OP]NvidiaI may have been getting too caught up in doing something super specific to my area (MLops or infrastructure). I haven't used too much REST or any Postman, so I'll look into both of those. Thanks, Alex!
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