Working with a large well known tech company, with surprisingly basic/non-existent ML until only very recently. Using Redshift to do a lot of the heavy lifting

Working with a large well known tech company, with surprisingly basic/non-existent ML until only very recently. Using Redshift to do a lot of the heavy lifting and initial data preparation, then SageMaker for hosting models and scoring, and Tableau for dashboards. While you can do training within SageMaker, we have a cluster of EC2 instances using H2O libraries (xgboost) to train, then wrap the resulting model as a docker image and deploy it to ECR and link to a SageMaker endpoint. Clunky and very much human-in-the-loop for training and deployment, but you can't run before you can crawl in this space.