Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps (Grayscale Indian Edition) Paperback – 28 October 2020

"ML Design Patterns" is a misleading title.I was very excited to read it cover-to-cover after checking the title, and that the authors drew parallels to Design Patterns in Software Engineering.Their patterns looked more like hacks/tricks than Design Patterns. I still am not sure what/how exactly Design Pattern should be -- but certainly they should go beyond proving tips and tricks.For example, I was more interested in "problem abstractions" and then provide a map to a "solution template". Lot of techniques that exist in the wild today can be mapped to simple problem types, and as a results, same technique can be applied, and avoid reinventing the wheel -- think of "canonical" forms in the optimisation literature. Similar things exists in Statistics literature also. From models persepctive, the likes of Linear Models, Generalized Linear Models, Structural Equation Models, Seemingly Unrelated Regressions, Measurement-Error-in-Predcictors etc. There are quite a many. While it is impossible to create a taxonomy out of it, at least, I hoped some exercise in that direction would have taken place.What I was looking for in Design Patterns is:<IF> your response is binary (0/1), both features are categorical, objective is to predict responses at the unobserved combinations.<Then>Solution templateThe above problem is a "pattern" -- Collaborative Filtering, Netflix movie type, Item Response Theory, Logistic Regression -- all are different names they go with, depending on the reader's familiarity/ domain knowledge.The above is simply the "model" dimension. They are are other dimensions concerned with data, pre-processing, evaluation etc.In summary, I think that Design Patterns is a too strong word they used and probably they have not done justice to it. Instead, someone should read it as a cookbook of ML Best Practices, and things to watch out for, while implementing (not so much of a design). From that perspective, this book does justice.

Love how the authors have characterised, classified and looked at different design patterns, and described what they are and how they are helpful. Revisiting ideas in this book over time and applying these ideas to actual problems can help one become a better ML engineer / data scientist, IMHO. Kudos to the authors on a great book.

It's a very nice book for understanding different designs ..what I like is the references to research papers implementin these designs...1 star less for the pricing.

It is an average book, not very impressive. I feel Design Machine Learning System is far better than this book.