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  • MLops
  • MLops
    • Chap 0. Before everything
    • Chap 1. Tools for developerschevron-right
    • Chap 2. Hardwarechevron-right
    • Chap 3. Infrastructure/platform designchevron-right
      • 3.1 Prototypechevron-right
      • 3.2 Productchevron-right
      • 3.3 Internal toolschevron-right
      • 3.4 Benchmarks
      • 3.5 Takeawayschevron-right
        • how to deal with failed driver
        • What to consider to upgrade a tool we used in the infra
        • backup plan when cloud infra failed
        • Some tips about data transferring between local and server
        • When to use cloud GPU or on-premise GPU
    • Chap 4. Toolkit/codebasechevron-right
    • Chap 5. Paper reproductionchevron-right
    • Chap 6. Prototype developmentchevron-right
    • Chap 7. Deployment and model servingchevron-right
    • Chap 8. Productionization/Maintenance/Adoptionchevron-right
    • Chap 9. PR/keep stoachevron-right
    • Acknowledge
  • DataOps
    • Chap 0. Preface
    • Chap 1. Data engineeringchevron-right
    • Chap 2. Data integration
    • Chap 3. Data security/privacy
    • Chap 4. Data quality
  • MODELOPS
    • Chap 0. Intro
    • Chap 1. Model registery
  • Fun Facts about Image
    • Chap 0. Preface
    • Chap 1. Processchevron-right
    • Chap 2. Metricschevron-right
    • Chap 3. Case Studychevron-right
  • Softskills
    • Chap 1. Mindsetschevron-right
    • Chap 2. Soft skills in getting things donechevron-right
    • Chap 3. Portfolio and side projectschevron-right
    • Chap 4. Mentorshipchevron-right
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  1. MLopschevron-right
  2. Chap 3. Infrastructure/platform design

3.5 Takeaways

https://isg-one.com/articles/building-the-right-enterprise-infrastructure-for-machine-learningarrow-up-right

Previous3.4 Benchmarkschevron-leftNexthow to deal with failed driverchevron-right

Last updated 5 years ago

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