Table on contents:

  • Introduction
    • Research vs production
      • Performance requirements
      • Compute requirements
  • Design a machine learning system
    • Project setup
    • Data pipeline
    • Modeling
      • Model selection
      • Training
      • Debugging
      • Hyperparameter tuning
      • Scaling
    • Serving
  • Case studies
  • Exercises

Don't forget to tag @chiphuyen in your comment, otherwise they may not be notified.

Authors
ML production pipeline. Taught "TensorFlow for Deep Learning Research" @Stanford. Author of 4 bestselling books. Writing ML Interviews Book. Insta: huyenchip19
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