60 minute session
Moving advanced analytics to your SQL Server databases
Where traditional analytics workflows often revolve around moving data to external tools, this session shows how you can instead bring models closer to the data by making use of in-database machine learning in SQL Server.
Attendees gain insight into how this approach leads to better performance, less data movement and possibilities for near real-time model scoring. In addition, practical implementations are covered, such as training and running models within SQL Server and integrating Azure Machine Learning with on-premises data.
Learning goals
- Understanding how traditional analytics workflows work and what the limitations are
- Gaining insight into the concept of SQL Server in-database analytics and its benefits
- Getting acquainted with SQL Server Machine Learning Services (R and Python)
- Learning how to train and apply models within SQL Server
- Understanding how to use stored procedures such as sp_execute_external_script and PREDICT
- Gaining insight into running real-time or near real-time predictions
- Understanding how to integrate Azure Machine Learning with SQL Server
Level
Intermediate
Materials