mlops
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  • MLops
  • MLops
    • Chap 0. Before everything
    • Chap 1. Tools for developers
    • Chap 2. Hardware
    • Chap 3. Infrastructure/platform design
      • 3.1 Prototype
      • 3.2 Product
      • 3.3 Internal tools
      • 3.4 Benchmarks
      • 3.5 Takeaways
        • 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/codebase
    • Chap 5. Paper reproduction
    • Chap 6. Prototype development
    • Chap 7. Deployment and model serving
    • Chap 8. Productionization/Maintenance/Adoption
    • Chap 9. PR/keep stoa
    • Acknowledge
  • DataOps
    • Chap 0. Preface
    • Chap 1. Data engineering
    • 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. Process
    • Chap 2. Metrics
    • Chap 3. Case Study
  • Softskills
    • Chap 1. Mindsets
    • Chap 2. Soft skills in getting things done
    • Chap 3. Portfolio and side projects
    • Chap 4. Mentorship
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  1. MLops
  2. Chap 3. Infrastructure/platform design

3.5 Takeaways

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

Previous3.4 BenchmarksNexthow to deal with failed driver

Last updated 4 years ago

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