Most data scientists bounce back and forth regularly between doing analysis in databases using SQL and building and deploying machine learning pipelines in R or python. But if we think ahead a few years, a few visionary researchers are starting to see a world in which the ML pipelines can actually be deployed inside the database. Why? One strong advantage for databases is they have built-in features for data governance, including things like permissioning access and tracking the provenance of data. Adding machine learning as another thing you can do in a database means that, potentially, these enterprise-grade features will be available for ML models too, which will make them much more widely accepted across enterprises with tight IT policies. The papers this week articulate the gap between enterprise needs and current ML infrastructure, how ML in a database could be a way to knit the two closer together, and a proof-of-concept that ML in a database can actually work.

Relevant links:
https://blog.acolyer.org/2020/02/19/ten-year-egml-predictions/
https://blog.acolyer.org/2020/02/21/extending-relational-query-processing/

Putting machine learning into a database

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