Extending relational query processing with ML inference
Extending relational query processing with ML inference, Karanasos, CIDR’10
This paper provides a little more detail on the concrete work that Microsoft is doing to embed machine learning inference inside an RDBMS, as part of their vision for Enterprise Grade Machine Learning. The motivation is not that inference will perform better inside the database, but that the database is the best place to take advantage of enterprise features (transactions, security, auditing, HA, and so on). Given the desire to keep enterprise data within the database, and to treat models as data also, the question is can we do inference in the database with acceptable performance? Raven is the system that Microsoft built to explore this question, and answer it with a resounding yes.
… based on interactions with enterprise customers, we expect that storage and inference of ML models will be subject to the same scrutiny and performance requirements of sensitive/mission-critical operational data. When it comes to data, database management systems (DBMSs) have been the trusted repositories for the enterprise… We thus propose to store and serve ML models from within the DBMS…
The authors don’t just mean farming inference out to an external process from within the RDBMS, Continue reading
