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Key benefits

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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

€36.99

€36.99

Data Engineering with Google Cloud Platform

€52.99

Total € 126.97

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Amazon verified reviews

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.

Amazon Verified review

Michael Jun 21, 2021

I used this product to quickly deepen my knowledge on GCP's BigQuery for work. At almost 400 pages I initially thought I would be skimming over certain sections because of BigQuery's product positioning among an already crowded Data Warehouse space.After a chapter on introductions and setup of projects, this book adroitly focuses on BigQuery's growing suite of ML/ AI tools and implementations. UI pictures are mostly up to date and labelled with explanations to such an extent that I feel confident using this after the 2021 BigQuery UI refresh and AI Platform refresh (Vertex AI). Also included are code snippets that in print show clearly, and if you have a digital copy of the book offer a accelerated path to practice by letting you copy and paste. I found it also useful for the book to use BigQuery free datasets to keep ancillary cloud costs down when I am running the exact same queries.As this technology grows in adoption, especially in the Adtech, Martech, and customer service verticals, I feel I will be going back to this BigQuery ML book more than my Oreilly paperback on Machine Learning Design Patterns. Although other books may have been made by Google Cloud experts, this book is made for the future experts. A novice individual, or already experienced cloud individual can exclusively use this book to acquire deep experience in BigQuery and a few other cloud services such as a data visualization tool, to boot.If you need to deepen your BigQuery expertise on the job this should be added to your list. If you are new to BigQuery and need to become an expert, congratulations! You now could only use a single, and this book is it.

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!

Lak Lakshmanan Jul 23, 2021

BigQuery ML is a way for business analysts with SQL skills to be able to do predictive analytics on data in their data warehouse. The idea is to provide high quality ML in an easy to use way without Python/Tensorflow/ETL etc.This book provides a step-by-step introduction for business analysts to get full advantage of BigQuery ML. Each model type is introduced with a typical business problem and then you are given step-by-step introductions to train and predict using a public dataset. So, the book is very self contained, and it should be able to adapt the given queries to your own business problem and data.If you are an ML scientist or a data engineer, this book will not be enough. It doesn't cover all the available options such as Regularization, transformation, or Preprocessing. Follow this book with reading either the docs directly or the ML chapter in my book "BigQuery The Definitive Guide"

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.

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

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