Supervised learning with Microsoft ML Services for in-database architecture
Speaker: Ronny Meeusen
Duration: 1 hour
Data scientists conquering more and more territory in todays organizations. The insights they give have made a big contribution to the company’s success. The experimental and researching face is now shifting towards automated processes where business processes can benefit from this new knowledge. With Microsoft ML services we can build and integrate the data scientists work into our existing architecture directly on top of your database using the in-database integration facility.
In this session, I explain one possible solution for creating a supervised learning model on an existing SQL server 2017 installation, the automation for the model regeneration, retraining, rescoring, versioning of the model and finally the usage of it in your SQL code. Some architectural questions regarding the choice between Azure ML or In-Database implementation will be explained.