The biggest feature in the SQL Server 2019 preview launched at Ignite is SQL Server Big Data clusters. Travis Wright, Microsoft's principal program manager for SQL Server, explains exactly what this means for administrators. Microsoft introduced a new community technology preview (CTP) of SQL Server 2019 at Microsoft Ignite on Monday (you can read about the full list of announced features here). As part of that announcement came SQL Server Big Data clusters, a scale-out, data virtualization platform built on top of the Kubernetes (K8s) container platform. SQL Server Big Data clusters is a big investment from Microsoft into a number of technologies -- and it is clear that taking one of its best-selling enterprise products and building on top of the K8s infrastructure is a moonshot at modernizing the data estate in most enterprises.

If you are a data scientist, business analyst or a machine learning engineer, you need model management – a system that manages and orchestrates the entire lifecycle of your learning model. Analytical models must be trained, compared and monitored before deploying into production, requiring many steps to take place in order to operationalize a model's lifecycle. In this blog, I will describe how SQL Server can enable you to automate, simplify and accelerate machine learning model management at scale – from build, train, test and deploy all the way to monitor, retrain and redeploy or retire. SQL Server treats models just like data – storing them as serialized varbinary objects. As a result, it is pretty agnostic to the analytics engines that were used to build models, thus making it a pretty good model management tool for not only R models (because R is now built-in into SQL Server 2016) but for other runtimes as well.