mlops
Ctrlk
  • MLops
  • MLops
    • Chap 0. Before everything
    • Chap 1. Tools for developers
    • Chap 2. Hardware
    • Chap 3. Infrastructure/platform design
      • 3.1 Prototype
        • maintenance@docker
        • Docker with GPU
        • Set up DL environment and versions/dependencies
        • GPU scheduler
        • Data version control
        • Data Parallism
        • maintenance@GPU
        • Training pipeline
        • maintenance@data
        • maintenance@storage
        • Model registry
        • A few good-to-have for training data tracking
      • 3.2 Product
      • 3.3 Internal tools
      • 3.4 Benchmarks
      • 3.5 Takeaways
    • 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. 3.1 Prototype

Model registry

ML in production:

https://mlinproduction.com/

Model registry:

https://mlinproduction.com/model-registries-for-ml-deployment-deployment-series-06/

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Last updated 4 years ago

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