(Thread) Here is a short summary of "Rules of ML" by Martin Zinkevich (developers.google.com/machine-learni…). The #MachineLearning life cycle should consist of three phases:
Phase 0
- Launch the product without #MachineLearning . Collect #Data
Phase 1
- Building the first basic/transparent #model
- Make the #infrastructure right
- Monitor the right set of #metrics
- System should get a boost with a simple model itself
- Launch the product without #MachineLearning . Collect #Data
Phase 1
- Building the first basic/transparent #model
- Make the #infrastructure right
- Monitor the right set of #metrics
- System should get a boost with a simple model itself
Phase 2
- More #FeatureEngineering on top of simple model (Low hanging fruits). #Regularization.
- Multiple launches with new features
- Metrics should still be rising with each launch
- More #FeatureEngineering on top of simple model (Low hanging fruits). #Regularization.
- Multiple launches with new features
- Metrics should still be rising with each launch
Phase 3
- Metrics have started hitting a plateau
- Complex models (Complex #Ensembles, #DeepLearning )
- Metrics have started hitting a plateau
- Complex models (Complex #Ensembles, #DeepLearning )
• • •
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