Tomaz Kastrun gives us an example using native scoring in SQL Server 2017 Machine Learning Services:
Native scoring in SQL Server 2017 comes with couple of limitations, but also with a lot of benefits. Limitations are:
currently supports only SQL server 2017 and Windows platform
trained model should not exceed 100 MiB in size
Native scoring with PREDICT function supports only following algorithms from RevoScaleR library:
rxLinMod (linear model as linear regression)
rxLogit (logistic regression)
rxBTrees (Parallel external memory algorithm for Stochastic Gradient Boosted Decision Trees)
rxDtree (External memory algorithm for Classification and Regression Trees
rxDForest (External memory algorithm for Classification and Regression Decision Trees)
Read on for an example. If you’re using one of these methods, then native scoring is extremely fast and a bit more flexible than I originally anticipated. The problem is that you have to use one of those methods.