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.

ML production pipeline. Taught "TensorFlow for Deep Learning Research" @Stanford. Author of 4 bestselling books. Writing ML Interviews Book. Insta: huyenchip19
Share this project
Similar projects
How (And Why) to Create a Good Validation Set
Steps for creating a representative validation set for training.
Machine Learning: Tests and Production
Best practices for testing ML-based systems.
Hidden Technical Debt in Machine Learning Systems
Using the software engineering framework of technical debt, we find it is common to incur massive ongoing maintenance costs in real-world ML systems.
Deep Learning for Anomaly Detection
Techniques and applications of anomaly detection.
Top collections