Quickstart: Create and score a predictive model in R with SQL Server Machine Learning Services

APPLIES TO: yesSQL Server noAzure SQL Database noAzure Synapse Analytics (SQL DW) noParallel Data Warehouse

In this quickstart, you'll create and train a predictive model using R, save the model to a table in your SQL Server instance, then use the model to predict values from new data using SQL Server Machine Learning Services.

You'll create and execute two stored procedures running in SQL. The first one uses the mtcars dataset included with R and generates a simple generalized linear model (GLM) that predicts the probability that a vehicle has been fitted with a manual transmission. The second procedure is for scoring - it calls the model generated in the first procedure to output a set of predictions based on new data. By placing R code in a SQL stored procedure, operations are contained in SQL, are reusable, and can be called by other stored procedures and client applications.


If you need a refresher on linear models, try this tutorial which describes the process of fitting a model using rxLinMod: Fitting Linear Models

By completing this quickstart, you'll learn:

  • How to embed R code in a stored procedure
  • How to pass inputs to your code through inputs on the stored procedure
  • How stored procedures are used to operationalize models


  • This quickstart requires access to an instance of SQL Server with SQL Server Machine Learning Services with the R language installed.

    Your SQL Server instance can be in an Azure virtual machine or on-premises. Just be aware that the external scripting feature is disabled by default, so you might need to enable external scripting and verify that SQL Server Launchpad service is running before you start.

  • You also need a tool for running SQL queries that contain R scripts. You can run these scripts using any database management or query tool, as long as it can connect to a SQL Server instance, and run a T-SQL query or stored procedure. This quickstart uses SQL Server Management Studio (SSMS).

Create the model

To create the model, you'll create source data for training, create the model and train it using the data, then store the model in a SQL database where it can be used to generate predictions with new data.

Create the source data

  1. Open SSMS, connect to your SQL Server instance, and open a new query window.

  2. Create a table to save the training data.

    CREATE TABLE dbo.MTCars(
        mpg decimal(10, 1) NOT NULL,
        cyl int NOT NULL,
        disp decimal(10, 1) NOT NULL,
        hp int NOT NULL,
        drat decimal(10, 2) NOT NULL,
        wt decimal(10, 3) NOT NULL,
        qsec decimal(10, 2) NOT NULL,
        vs int NOT NULL,
        am int NOT NULL,
        gear int NOT NULL,
        carb int NOT NULL
  3. Insert the data from the built-in dataset mtcars.

    INSERT INTO dbo.MTCars
    EXEC sp_execute_external_script @language = N'R'
        , @script = N'MTCars <- mtcars;'
        , @input_data_1 = N''
        , @output_data_1_name = N'MTCars';


    Many datasets, small and large, are included with the R runtime. To get a list of datasets installed with R, type library(help="datasets") from an R command prompt.

Create and train the model

The car speed data contains two columns, both numeric: horsepower (hp) and weight (wt). From this data, you'll create a generalized linear model (GLM) that estimates the probability that a vehicle has been fitted with a manual transmission.

To build the model, you define the formula inside your R code, and pass the data as an input parameter.

    EXEC sp_execute_external_script
    @language = N'R'
    , @script = N'carsModel <- glm(formula = am ~ hp + wt, data = MTCarsData, family = binomial);
        trained_model <- data.frame(payload = as.raw(serialize(carsModel, connection=NULL)));'
    , @input_data_1 = N'SELECT hp, wt, am FROM MTCars'
    , @input_data_1_name = N'MTCarsData'
    , @output_data_1_name = N'trained_model'
    WITH RESULT SETS ((model VARBINARY(max)));
  • The first argument to glm is the formula parameter, which defines am as dependent on hp + wt.
  • The input data is stored in the variable MTCarsData, which is populated by the SQL query. If you don't assign a specific name to your input data, the default variable name would be InputDataSet.

Store the model in the SQL database

Next, store the model in a SQL database so you can use it for prediction or retrain it.

  1. Create a table to store the model.

    The output of an R package that creates a model is usually a binary object. Therefore, the table where you store the model must provide a column of varbinary(max) type.

    CREATE TABLE GLM_models (
        model_name varchar(30) not null default('default model') primary key,
        model varbinary(max) not null
  2. Run the following Transact-SQL statement to call the stored procedure, generate the model, and save it to the table you created.

    INSERT INTO GLM_models(model)
    EXEC generate_GLM;


    If you run this code a second time, you get this error: "Violation of PRIMARY KEY constraint...Cannot insert duplicate key in object dbo.stopping_distance_models". One option for avoiding this error is to update the name for each new model. For example, you could change the name to something more descriptive, and include the model type, the day you created it, and so forth.

    UPDATE GLM_models
    SET model_name = 'GLM_' + format(getdate(), 'yyyy.MM.HH.mm', 'en-gb')
    WHERE model_name = 'default model'

Score new data using the trained model

Scoring is a term used in data science to mean generating predictions, probabilities, or other values based on new data fed into a trained model. You'll use the model you created in the previous section to score predictions against new data.

Create a table of new data

First, create a table with new data.

	, wt DECIMAL(10,3) NOT NULL
	, am INT NULL

INSERT INTO dbo.NewMTCars(hp, wt)
VALUES (110, 2.634)

INSERT INTO dbo.NewMTCars(hp, wt)
VALUES (72, 3.435)

INSERT INTO dbo.NewMTCars(hp, wt)
VALUES (220, 5.220)

INSERT INTO dbo.NewMTCars(hp, wt)
VALUES (120, 2.800)

Predict manual transmission

To get predictions based on your model, write a SQL script that does the following:

  1. Gets the model you want
  2. Gets the new input data
  3. Calls an R prediction function that is compatible with that model

Over time, the table might contain multiple R models, all built using different parameters or algorithms, or trained on different subsets of data. In this example, we'll use the model named default model.

DECLARE @glmmodel varbinary(max) = 
    (SELECT model FROM dbo.GLM_models WHERE model_name = 'default model');

EXEC sp_execute_external_script
    @language = N'R'
    , @script = N'
            current_model <- unserialize(as.raw(glmmodel));
            new <- data.frame(NewMTCars);
            predicted.am <- predict(current_model, new, type = "response");
            OutputDataSet <- cbind(new, predicted.am);
    , @input_data_1 = N'SELECT hp, wt FROM dbo.NewMTCars'
    , @input_data_1_name = N'NewMTCars'
    , @params = N'@glmmodel varbinary(max)'
    , @glmmodel = @glmmodel
WITH RESULT SETS ((new_hp INT, new_wt DECIMAL(10,3), predicted_am DECIMAL(10,3)));

The script above performs the following steps:

  • Use a SELECT statement to get a single model from the table, and pass it as an input parameter.

  • After retrieving the model from the table, call the unserialize function on the model.

  • Apply the predict function with appropriate arguments to the model, and provide the new input data.


In the example, the str function is added during the testing phase, to check the schema of data being returned from R. You can remove the statement later.

The column names used in the R script are not necessarily passed to the stored procedure output. Here the WITH RESULTS clause is used to define some new column names.


Result set for predicting properbility of manual transmission

It's also possible to use the PREDICT (Transact-SQL) statement to generate a predicted value or score based on a stored model.

Next steps

For more information on SQL Server Machine Learning Services, see: