Machine Learning Design Patterns

An occasional series of design patterns for ML Engineers

Note: This series has now become an O’Reilly book. You can read it in early release here on Safari or pre-order it on Amazon.

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Book, due to be published Nov. 2020

Original blogposts

Here are links to the blogposts that were expanded into the book (the book has 30 patterns):

  1. Checkpoints: Saving the intermediate weights of your model during training provides resilience, generalization, and tuneability
  2. Virtual epochs: Base machine learning model training and evaluation on total number of examples, not on epochs or steps
  3. Keyed predictions: Export your model so that it passes through client keys
  4. Repeatable sampling: use the hash of a well distributed column to split your data into training, validation, and testing

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