It's all fine and well until

I can't help but feel that a failure of this project is a failure of me as a professional

Indeed, the subtle art of being in academia is digging golden nuggets of knowledge from otherwise "failing" (well, most of the time, anyway) projects. Most of the time, if the spec is any detailed, meeting it is a largely retroactive effort - scaling an approach tried (and proven!) on a small sample to a larger one. Novel ideas usually don't work and often many man-months are "wasted" on them... That is, until you acknowledge the knowledge you have obtained as value.

The issue is not specific to ML/AI, what changes is you don't have your expectations as high in many other fields; the level of hype around ML has research tied much closely to real world applications where people expect workable results, something that may take decades in more "traditional" fields.

It's all fine and well until

I can't help but feel that a failure of this project is a failure of me as a professional

Indeed, the subtle art of being in academia is digging golden nuggets of knowledge from otherwise "failing" (well, most of the time, anyway) projects. Most of the time, if the spec is detailed enough - e.g. "achieve X with Y precision on Z dataset by time T", it is met by the virtue of the "unknown" portion of the work being already done. That is, no one claims to finish a completely novel and creative work in a given timeframe. New ideas usually don't work and often many man-months are "wasted" on them... That is, until you realize the value there is the knowledge obtained in the process.

The issue is not specific to ML/AI, what changes is you don't have your expectations as high in many other fields; the level of hype around ML has research tied much closely to real world applications where people expect workable results, something that may take decades in more "traditional" fields.