>whether or not what you're attempting to do is possible with the approach you've chosen
Knowing that depends on your level of understanding the field and the math behind and also experience. If you just know how to make API calls, then it's hard.
What would be problematic if you want to do sentiment analysis for some product reviews? Result is the public perception within a margin of error, you have your data, you know what you want, you know how to get there.
Well, even with a high level of understanding, any sufficiently advanced use case will still have some uncertainty regarding its "feasibility". Of course, you might think that some problems are "solved", e.g., OCR, translation, (common) object recognition, but MANY other problems exist where, no matter how experienced and knowledgeable you are, you can only have an educated guess as to whether a given model can achieve a given performance without actually trying it out.
Where experience and knowledge really pays off is in telling apart model performance from bugs. There is a real know-how in troubleshooting ML pipelines and models in general.
Knowing that depends on your level of understanding the field and the math behind and also experience. If you just know how to make API calls, then it's hard.
What would be problematic if you want to do sentiment analysis for some product reviews? Result is the public perception within a margin of error, you have your data, you know what you want, you know how to get there.