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Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you’ll work through a sample business decision by employing a variety of data science approaches.Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science.You’ll learn how to:Automate and schedule data ingest, using an App Engine applicationCreate and populate a dashboard in Google Data StudioBuild a real-time analysis pipeline to carry out streaming analyticsConduct interactive data exploration with Google BigQueryCreate a Bayesian model on a Cloud Dataproc clusterBuild a logistic regression machine-learning model with SparkCompute time-aggregate features with a Cloud Dataflow pipelineCreate a high-performing prediction model with TensorFlowUse your deployed model as a microservice you can access from both batch and real-time pipelines

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Product details

Paperback: 404 pages

Publisher: O'Reilly Media; 1 edition (January 16, 2018)

Language: English

ISBN-10: 1491974567

ISBN-13: 978-1491974568

Product Dimensions:

7 x 0.8 x 9.2 inches

Shipping Weight: 1.4 pounds (View shipping rates and policies)

Average Customer Review:

3.6 out of 5 stars

9 customer reviews

Amazon Best Sellers Rank:

#85,770 in Books (See Top 100 in Books)

Wow. A true tour of data science and engineering on the cloud.It's been a few years since I've worked with tools in this field, but this book was a clear level-headed view for data engineers looking to derive and drive insights from data. Using a core example use case and following it end to end through the entire book (and indeed cloud tools integrated with each other) helped me keep track of what was going on, and kept things from becoming a book on theory rather than one of accomplishment and answers. The purpose and process for each tool was clear, and I also appreciated the explanations of trade-offs and the value added for the choices made. The practice of data science is a LOT easier now with cloud/serverless tools than eight or nine years ago, and I feel this brought me back to the state of the art.

While Lakâ€™s conversational style can be a turn off to some who just want an answer and donâ€™t care about how, I liked this book. Many times with books like this you get an answer or a recipe and youâ€™re done. What happens when your answer or recipe isnâ€™t right for the situation? Iâ€™m glad Lak explains his rationale and letâ€™s it be known that thereâ€™s more than one way to do it. Could the book have been condensed without the explanations? Yes. Would it have been like almost every other book in the space? Yes. Check out this book if you want a well thought out answer and maybe alternates. If you just want the â€œright answerâ€, then buy something else.

The book is easy to follow with detailed descriptions of each step followed to build a project from start to end on the Google Cloud Platform.The book is also accompanied by a code repository which lets the readers try out the project themselves.Strongly recommended for data scientists learning to use the platform.

Wonderful book filled with great examples and very engaging writing style! I particularly appreciated how realistic the examples are and was able to use many of the code examples to bootstrap my own projects.

This book was a sad disappointment. The author goes on and on, in long sentences, on unrelated statements instead of addressing the fundamentals of GCP. A waste of time and money. The incentives for publishers to release catchy titles and bloated electronic content on high-priced tags are clear: profits by deception.

Really nice, good price

Very interesting and well written

Very easy to consume because written as a story

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