To get ML models into production is a lot of work involving tests, retraining, model iteration/management and model serving (often at the edge where latency is

To get ML models into production is a lot of work involving tests, retraining, model iteration/management and model serving (often at the edge where latency is low). ML-Ops is as deep as data engineering but not as often talked about. The linked article discusses one example.