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Gain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery ML

Leverage SQL syntax to train, evaluate, test, and use ML models

Discover how BigQuery works and understand the capabilities of BigQuery ML using examples

Description

BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML. The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement. By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML.

Who is this book for?

This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. You'll also find this book useful if you want to accelerate the development of ML models or if you are a business user who wants to apply ML in an easy way using SQL. Basic knowledge of BigQuery and SQL is required.

What you will learn

Discover how to prepare datasets to build an effective ML model

Forecast business KPIs by leveraging various ML models and BigQuery ML

Build and train a recommendation engine to suggest the best products for your customers using BigQuery ML

Develop, train, and share a BigQuery ML model from previous parts with AI Platform Notebooks

Find out how to invoke a trained TensorFlow model directly from BigQuery

Get to grips with BigQuery ML best practices to maximize your ML performance

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Jignesh Mehta Sep 14, 2021

BigQuery, as a leading cloud data warehouse platform, offers many benefits to data analytics community with respect to performance at scale but the jewel of the crown is its built-in ML capabilities! Bringing ML modeling in hands of SQL developers and where the data resides is very powerful capabilities for accelerated advanced analytics applications!This book is a perfect resource for any data analytics developer and data scientists to quickly go from zero to sixty in a week for building ML based solutions. The Book is well focused with practical approach to machine learning models within BigQuery by providing excellent use cases to work with. One can easily apply the learning to the business problem at hand. I particularly liked the flow of the book, even within a chapter, to go from introduction to advanced concepts.A complete guide to BigQuery Machine Learning for anyone who wants to solve business problem with sophisticated machine learning algorithm but with ease of SQL statements!

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Naveen Mathew Nathan S. Sep 14, 2021

I got interested in the book once I found out about new functionalities in Google Cloud Platforms - especially about triggering models and model management using Big Query. The explanations and examples were light weight and easy to understand compared to online courses on the same topic(s). Coding along is strongly recommended. For GCP certification a more advanced course or book may be required to complement the contents of this book.

Aarti Jul 06, 2021

The book is well divided into 4 sections swiftly moving to the advanced areas. What I really enjoyed about the book is that it covers most of the aspects of BigQuery. I feel the book is designed in a manner that people with novice knowledge on the topic can master it, a concise guide. The matrix factorization and forecasting timeseries were very well explained and are highly useful for me. Overall a good book to build ML models would really recommend it.

Deepak Jun 18, 2021

As a Big query practitioner, I found the book very fascinating. The concept are concise with interesting problem statement to work on. I feel, the book is designed in such a way that you will be getting your basics clear and then move on to the difficult part. BigQuery cost depends on size of data getting scanned and queries mentioned in the book gives you the best cost optimization. I would highly recommend to refer this book who are exploring BigQuery and ML related problem statement.

Walter Lee Sep 06, 2021

This is a very easy to read intro. level book to get you started using BQML the easy way. It shows you step by step guide to prep/clean/filter the data, train the model with many different supported algorithms/options, evaluate the model (e.g. no overfitting) and predict with the trained model. It is an excellent book if you like to use standard SQL to do Machine Learning without the need to learn Python, Tensorflow, etc… This will help liberate many business intelligence (BI) specialists upgraded into the next level, i.e. able to predict and classify for good business use cases with BQML machine learning models. A lot of easy to use code to get started. A lot of references to learn more in BQML and other use cases.Section 1 is more introduction to GCP and BQ environment. You can skip if you know them already.Section 2 is more on the linear regression, binary and multiclass logistic classifications.Section 3 is more on Adv. Models, e.g. K-means, matrix factorization, XGBoost, Deep Neural Networks.Section 4 is more on how to use GCP AI notebooks to run BQML, run TF models with BQML (e.g. import/export BQ <-> TF saved model, BQML Tips and Best Practices, e.g. data quality, prep data, hyperparameters tuning, how to do near real time recommendations, etc.Suggestions:1/ there should be a typo in p. 324, “In this case, the MOD function returns a value from 0 to *10*”, Mod (X, 10) should return from 0-9 (not 10) because it returns the REMINDER of the Mod function after division.2/ Can introduce GCP Data Prep to help prepare and visualize the data in addition to Data Studio and its min/max/Not Null examples as shown in the book.

Machine Learning with BigQuery ML: Create, execute, and improve machine learning models in BigQuery using standard SQL queries

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