The elephant in the engineering room right now is that most AI failures aren’t about model accuracy or even the quality of your training data. Instead, many organizations aren’t getting what they want (or expect) out of AI because they’re trying to serve real-time predictions from a batch-processed data pipeline.
I’m hands-on with a lot of enterprises that have built impressive machine learning (ML) models that deliver sub-second inference times … and then feed them data that’s already hours old. The Ferrari engine has square…
