San Francisco-based database vendor Splice Machine is taking an integrated approach to enabling machine learning with its eponymous database. Splice Machine is a distributed SQL relational database management system that includes machine learning capabilities as part of the overall platform.

Splice Machine 3.0 became generally available on March 3, bringing with it updated machine learning capabilities. It also has new Kubernetes cloud native-based model for cloud deployment and enhanced replication features.

In this Q&A, Monte Zweben, co-founder and CEO of Splice Machine, discusses the intersection of machine learning and databases and provides insight into the big changes that have occurred in the data landscape in recent years.

How do you integrate machine learning capabilities with a database?

Instead of thinking about storing the data, companies should think about how to use the data. Start with the application and how you are going to make the application leverage new data sources.

The second reason why this was a mistake was organizationally, because the data scientists who know AI were all centralized in one data science group, away from the application. They are not the subject matter experts for the application.

When you focus on the application and retrofit the application to make it smart and inject AI, you can get a multidisciplinary team. You have app developers, architects, subject-matter experts, data engineers and data scientists, all working together on one purpose. That is a radically more effective and productive organizational structure for modernizing applications with AI.

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