Microsoft SQL Server 2017 (and later) with Machine Learning Services already do in-database ML [1]. Models are trained, stored and invoked via stored procedures which call R or Python code (SQL is not the best language to do ML in).
The advantage of this approach is that data is never moved outside SQL Server or over the network. The downside I guess is that you need a pretty beefy machine to run the database server.
For uncomplicated ML applications, say a logistic regression over a few columns, this is a relatively easy approach to get results quickly. To me, the actual use cases of in-db ML are limited, but the one case in which I can imagine it being useful is performing live ML on a SQL view that has constantly evolving data -- you save ETL roundtrips to an external ML algorithm.
The advantage of this approach is that data is never moved outside SQL Server or over the network. The downside I guess is that you need a pretty beefy machine to run the database server.
For uncomplicated ML applications, say a logistic regression over a few columns, this is a relatively easy approach to get results quickly. To me, the actual use cases of in-db ML are limited, but the one case in which I can imagine it being useful is performing live ML on a SQL view that has constantly evolving data -- you save ETL roundtrips to an external ML algorithm.
[1] https://docs.microsoft.com/en-us/sql/machine-learning/sql-se...