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As a first time user of Kubeflow, this book effectively paints its ecosystem and feature-set while using examples to solidify the reader's understanding. I personally am interested in the pipeline and serving components of Kubeflow and thought that this book enabled first time users whilst also catering to more sophisticated use cases. Overall super helpful and I highly recommend it!
This book is not really recommended. The first chapters are supposed to get you going with Kubeflow but they leave a lot of blanks and a lot of things are not thoughtfully explained. I found a couple of blog posts that would introduce me to the topic better. The rest of the book reads more like a library’s documentation than a book. I guess I would have been better off to directly read the docs and do my own research. Thought this would allow me to get going with Kubeflow faster than browsing the web. In conclusion, it didn’t.
No one will be able to contradict this: there is not yet a standard open source platform for machine learning. Kubelflow, kubernetes application, this offers. This is not a beginner's book, if you don't know how to set up a kubernetes cluster, if you don't know scikit learn / tensor flow, if you don't know apache spark: go your way.
Machine learning is young, like Linux and Java was, it's exciting
This book heads in a daring direction, and pulls along the tools and techniques we we will all need to use to marry our cloud native approach to development with our pipeline driven view of ML/DL, and brings forward the real scope of concerns one must address to do it. I really appreciate the detail.
Bought the paperback and disappointed that the images and code snippets are all black and white instead of full color like the Hands on Machine Learning book by Aurelien. Content wise it's a good overview packed with lots of useful info about ML in production. Just disappointed about the color. Wonder how the ebook is?