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by Valliappa Lakshmanan, Sara Robinson, Michael Munn
Machine Learning Design Patterns
1. The Need for Machine Learning Design Patterns
What Are Design Patterns?
Machine Learning Terminology
Models and Frameworks
Data and Feature Engineering
The Machine Learning Process
Data and Model Tooling
Roles
Common Challenges in Machine Learning
Data Quality
Reproducibility
Data Drift
Scale
Multiple Objectives
Machine Learning Systems
ML Lifecycle
AI Readiness
2. Data Representation Design Patterns
Simple Data Representations
Numerical Inputs
Categorical Inputs
Design Pattern 1: Hashed Feature
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design Pattern 2: Reframing
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design Pattern 3: Feature Cross
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design Pattern 4: Multimodal Input Representations
Problems
Solution
Tradeoffs and Alternatives
Summary
3. Problem Representation Design Patterns
Design Pattern 5: Reframing
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design Pattern 6: Multilabel
Problem
Solution
Trade Offs
Design Pattern 7: Ensembles
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design Pattern 8: Cascade
Problem
Solution
Tradeoffs and Alternatives
Design Pattern 9: Neutral Class
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design Pattern 10: Rebalancing
Problem
Solutions
Tradeoffs and Alternatives
Summary
4. Patterns That Modify Model Training
Typical Training Loop
Stochastic Gradient Descent
Keras Training Loop
Training Design Patterns
Design Pattern 11: Useful Overfitting
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design Pattern 12: Checkpoints
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design Pattern 13: Transfer Learning
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design Pattern 14: Distribution Strategy
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design Pattern 15: Hyperparameter Tuning
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Summary
5. Design Patterns for Resilient Serving
Design pattern 16: Stateless Serving Function
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design pattern 17: Batch Serving
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design pattern 18: Continued Model Evaluation
Problem
Solution
Why It Works
Tradeoffs and Alternatives
Design pattern 19: Two Phase Predictions
Problem
Solution
Tradeoffs and Alternatives
Design pattern 20: Keyed Predictions
Problem
Solution
Tradeoffs and Alternatives
Summary
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Machine Learning Design Patterns
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