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

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

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Bringing it all together

22 of 23

22 of 23

Bringing it all together

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

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

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