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Classroom Contents
Microsoft SQL Server Machine Learning Workloads on Red Hat Enterprise Linux and Red Hat OpenShift
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- 1 Intro
- 2 Session Evaluation
- 3 How businesses are using Al/ML
- 4 Al/ML Lifecycle and Key Personas
- 5 Key Execution Challenges
- 6 Fundamental Architecture Building Blocks
- 7 Red Hat and Microsoft enabled ML
- 8 Why AI/ML workloads need containers?
- 9 Why SQL Server Containers?
- 10 SQL Server on Linux Same
- 11 Bringing intelligence where the data lives
- 12 In-database Machine Learning
- 13 But, Where to run the containers?
- 14 Different personas have different needs
- 15 Meet the RHEL Container Tools
- 16 Why should you use Podman in RHEL?
- 17 Building for more agility and scale
- 18 Why OpenShift?
- 19 What's needed to put Kubernetes into production?
- 20 Red Hat OpenShift Kubernetes Platform
- 21 Microsoft SQL Server Big Data Clusters
- 22 Bringing it all together
- 23 Deliver intelligent apps faster with Red Hat + Microsoft
