Microsoft SQL Server Machine Learning Workloads on Red Hat Enterprise Linux and Red Hat OpenShift

This session explains how to use SQL Server’s native machine learning capabilities to preprocess data, discover features, and train models in-database without moving data. It covers running these workloads in containers with Red Hat Enterprise Linux and OpenShift, including SQL Server Big Data Clusters.

Syllabus

Intro
Session Evaluation
How businesses are using Al/ML
Al/ML Lifecycle and Key Personas
Key Execution Challenges
Fundamental Architecture Building Blocks
Red Hat and Microsoft enabled ML
Why AI/ML workloads need containers?
Why SQL Server Containers?
SQL Server on Linux Same
Bringing intelligence where the data lives
In-database Machine Learning
But, Where to run the containers?
Different personas have different needs
Meet the RHEL Container Tools
Why should you use Podman in RHEL?
Building for more agility and scale
Why OpenShift?
What's needed to put Kubernetes into production?
Red Hat OpenShift Kubernetes Platform
Microsoft SQL Server Big Data Clusters
Bringing it all together
Deliver intelligent apps faster with Red Hat + Microsoft