Integrating SQL Server and ML

Of course, you already know that you can execute R and Python code from the T-SQL code. With SQL Server 2016 and 2017, you get a highly scalable ML engine. You install this engine with SQL Server installation by selecting the ML Services (In-database), as I explained in Chapter 1, Writing Queries with T-SQL. With Microsoft libraries, you get a lot of parallelized functions that utilize this scalable engine. You can use these functions for huge datasets. You can store a machine-learning model created with R or Python in a SQL Server table in a binary column. You use the stored models for predictions on new data. If you save an R or Python graph to a binary column, you can use it in SQL Server Reporting Services (SSRS) reports. Besides SQL Server, other Microsoft products and services support Python and R Power BI Desktop, Power BI Service, and Azure...