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

Explore the integration of machine learning capabilities in Microsoft SQL Server running on Red Hat Enterprise Linux and Red Hat OpenShift in this 53-minute conference talk from PASS Data Community Summit. Learn how to accelerate the entire ML pipeline in-database, including data preprocessing, feature discovery, and model training without data migration. Discover the benefits of using containers for efficient ML model production and understand the role of Microsoft SQL Server Big Data Clusters on Red Hat OpenShift. Gain insights into the AI/ML lifecycle, key execution challenges, and fundamental architecture building blocks. Examine the advantages of SQL Server containers, in-database machine learning, and the RHEL Container Tools. Understand the importance of Kubernetes in production environments and how Red Hat OpenShift Kubernetes Platform enhances agility and scale. Explore the synergy between Red Hat and Microsoft technologies to deliver intelligent applications faster.

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