Optimization tips and tricks on Azure SQL Server for Machine Learning Services

Since SQL Server 2016, a new function called R Services has been introduced. Microsoft recently announced a preview for the next version of SQL Server, which extends the advanced analytical ability to Python. This new capability of running R or Python in-database at scale enables us to keep the analytics services close to the data and eliminates the burden of data movements. It also simplifies the development and deployment of intelligent applications. To get the most out of SQL server, knowing how to fine tune the intelligence model itself is far from sufficient and sometimes still fail to meet the performance requirement. There are quite a few optimization tips and tricks that could help us boost the performance significantly. In this post, we apply a few optimization techniques to a resume-matching scenario, which mimics the workflow of large volume prediction aiming to showcase how those techniques could make data analytics more efficient and powerful. The three main optimization techniques introduced in our blog are as follows:

 

• Full durable memory-optimized tables
• CPU affinity and memory allocation
• Resource governance and concurrent execution

 

Read about it on the Azure blog.