"If you want to build novel things, you really need academic-grade ML." is a bit tricky.
If you want to achieve novel (better than yesterday's state of art) results on existing problems, then yes, you really need academic grade ML. Especially for "solved" (i.e. well researched) problems - if the current solution isn't good enough for your needs, then you're going to need serious work to improve on that.
However, if you want to attack novel business problems, then it's quite likely that you can solve them without needing to solve any new ML problems. You have to know what "instruments" are available, and you have to be able to read&learn how implement a particular solution that you choose, but generally you just need to squint hard enough to map your business problem to one or more ML tasks that have a known solution.
If you want to achieve novel (better than yesterday's state of art) results on existing problems, then yes, you really need academic grade ML. Especially for "solved" (i.e. well researched) problems - if the current solution isn't good enough for your needs, then you're going to need serious work to improve on that.
However, if you want to attack novel business problems, then it's quite likely that you can solve them without needing to solve any new ML problems. You have to know what "instruments" are available, and you have to be able to read&learn how implement a particular solution that you choose, but generally you just need to squint hard enough to map your business problem to one or more ML tasks that have a known solution.