Microsoft introduced native predictive model scoring with the release of SQL Server 2017.
The PREDICT function (Documentation) is now a native T-SQL function that eliminates having to score using R or Python through the sp_execute_external_script procedure. It's an alternative to sp_rxPredict. In both cases you do not need to install R but with PREDICT you do not need to enable SQLCLR either - it's truly native.
PREDICT should make predictions much faster as the process avoids having to marshal the data between SQL Server and Machine Learning Services (Previously R Services).
Migrating from the original sp_execute_external_script approach to the new native approach tripped me up so I thought I'd share a quick summary of what I have learned.
Error occurred during execution of the builtin function 'PREDICT' with HRESULT 0x80004001.
Model type is unsupported.
Not all models are supported. At the time of writing, only the following models are supported:
sp_rxPredict supports additional models including those available in the MicrosoftML package for R (I was using attempting to use rxFastTrees). I presume this limitation will reduce over time. The list of supported models is referenced in the PREDICT function (Documentation).
Error occurred during execution of the builtin function 'PREDICT' with HRESULT 0x80070057.
Model is corrupt or invalid.
The serialisation of the model needs to be modified for use by PREDICT. Typically you might serialise your model in R like this:
model <- data.frame(model=as.raw(serialize(model, NULL)))
Instead you need to use the rxSerializeModel method:
model <- data.frame(rxSerializeModel(model, realtimeScoringOnly = TRUE))
There's a corresponding rxUnserializeModel method, so it's worth updating the serialisation across the board so models can be used interchangeably in the event all models are eventually supported. I have been a bit legacy.
That's it. Oh, apart from the fact PREDICT is supported in Azure SQL DB, despite the documentation saying the contrary.