The field of ML is largely focused on just getting predictions with fancy models. Estimating the uncertainty, unexpectedness and perplexity of specific predictions is highly underappreciated in common practice.
Even though it is highly economically valuable to be able to tell to what extent you can trust a prediction, the modelling of uncertainty of ML pipelines remains an academic affair in my experience.
The field of ML is largely focused on just getting predictions with fancy models. Estimating the uncertainty, unexpectedness and perplexity of specific predictions is highly underappreciated in common practice.
Even though it is highly economically valuable to be able to tell to what extent you can trust a prediction, the modelling of uncertainty of ML pipelines remains an academic affair in my experience.