AI Inference Workloads - Solving MLOps Challenges in Production

AI Inference Workloads - Solving MLOps Challenges in Production

Toronto Machine Learning Series (TMLS) via YouTube Direct link

Intro

1 of 17

1 of 17

Intro

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AI Inference Workloads - Solving MLOps Challenges in Production

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  1. 1 Intro
  2. 2 Agenda
  3. 3 The Machine Learning Process
  4. 4 Deployment Types for Inference Workloads
  5. 5 Machine Learning is Different than Traditional Software Engineering
  6. 6 Low Latency
  7. 7 High Throughput
  8. 8 Maximize GPU Utilization
  9. 9 Embedding ML. Models into Web Servers
  10. 10 Decouple Web Serving and Model Serving
  11. 11 Model Serving System on Kubernetes
  12. 12 Multi-Instance GPU (MIG)
  13. 13 Run:Al's Dynamic MIG Allocations
  14. 14 Run 3 instances of type 2g.10gb
  15. 15 Valid Profiles & Configurations
  16. 16 Serving on Fractional GPUs
  17. 17 A Game Changer for Model Inferencing

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