What are common industry methods to measure model uncertainty?

I know bootstrapping can give you some measure of variance of an estimator.

But my exposure to this is only in the academic setting. What are people using in the industry for classical ML models and time series predictions?

I also am familiar with bayesian methods but would love to hear about practical and actual methods people have used before to measure model uncertainty for both classifiers and regressors

academic bayesian bootstrapping datascience industry love ml models people predictions series time series uncertainty variance