Great to see ML "governance" work being done on the training-part of the pipeline. Seems like this provides a Domino Data Labs based dashboards but without the

Great to see ML "governance" work being done on the training-part of the pipeline. Seems like this provides a Domino Data Labs based dashboards but without the walled garden environment. I've yet to see similar great initiatives also tackling the deployment-part. E.g. something similar you can stick on top of your model's API (or scheduled batch predictive outputs), as well as incoming instances, to monitor usage patterns, population shifts through time, probability distributions, newly popping up missing values or categorical levels, logs, etc, in order to provide warning lights to indicate that a retraining might be in order, for instance. Google's "What's your ML test score" paper provides some great insights, but I hope someone will tackle this with a turnkey solution as well.