There are many “last mile” items on the enterprise checklist, and companies are struggling to connect everything together. In this episode, Monte Zweben, CEO of Splice Machine, discusses feature stores with Andy Thurai and Stephen Foskett. Data engineers maintain data pipelines, data scientists maintain the data store, and machine learning engineers are trying to create models and package them so they will be useful. One idea is to store a model in a relational database, store records in a feature table, and enable the database to trigger a model based on this data. That’s what Splice Machines is implementing – in-database ML deployment. SQL is making a comeback in ML, with scale-out solutions providing a more familiar and usable environment than leading noSQL databases. Monte believes that SQL will be the dominant data paradigm for machine learning, modeling, experimentation, and deployment. After all, SQL is the dominant language of enterprise data scientists.