Hands-On Artificial Intelligence on Google Cloud Platform
Build intelligent applications powered by TensorFlow, Cloud AutoML, BigQuery, and Dialogflow
Published on 6. March 2020
Book
Paperback/Softback
350 pages
978-1-78953-846-5 (ISBN)
Description
Develop robust AI applications with TensorFlow, Cloud AutoML, TPUs, and other GCP services
Key Features
Focus on AI model development and deployment in GCP without worrying about infrastructure
Manage feature processing, data storage, and trained models using Google Cloud Dataflow
Access key frameworks such as TensorFlow and Cloud AutoML to run your deep learning models
Book Description
With a wide range of exciting tools and libraries such as Google BigQuery, Google Cloud Dataflow, and Google Cloud Dataproc, Google Cloud Platform (GCP) enables efficient big data processing and the development of smart AI models on the cloud. This GCP book will guide you in using these tools to build your AI-powered applications with ease and managing thousands of AI implementations on the cloud to help save you time.
Starting with a brief overview of Cloud AI and GCP features, you'll learn how to deal with large volumes of data using auto-scaling features. You'll then implement Cloud AutoML to demonstrate the use of streaming components for performing data analytics and understand how Dialogflow can be used to create a conversational interface. As you advance, you'll be able to scale out and speed up AI and predictive applications using TensorFlow. You'll also leverage GCP to train and optimize deep learning models, run machine learning algorithms, and perform complex GPU computations using TPUs. Finally, you'll build and deploy AI applications to production with the help of an end-to-end use case.
By the end of this book, you'll have learned how to design and run experiments and be able to discover innovative solutions without worrying about infrastructure, resources, and computing power.
What you will learn
Understand the basics of cloud computing and explore GCP components
Work with the data ingestion and preprocessing techniques in GCP for machine learning
Implement machine learning algorithms with Google Cloud AutoML
Optimize TensorFlow machine learning with Google Cloud TPUs
Get to grips with operationalizing AI on GCP
Build an end-to-end machine learning pipeline using Cloud Storage, Cloud Dataflow, and Cloud Datalab
Build models from petabytes of structured and semi-structured data using BigQuery ML
Who this book is for
If you're an artificial intelligence developer, data scientist, machine learning engineer, or deep learning engineer looking to build and deploy smart applications on Google Cloud Platform, you'll find this book useful. A fundamental understanding of basic data processing and machine learning concepts is necessary. Though not mandatory, familiarity with Google Cloud Platform will help you make the most of this book.
Key Features
Focus on AI model development and deployment in GCP without worrying about infrastructure
Manage feature processing, data storage, and trained models using Google Cloud Dataflow
Access key frameworks such as TensorFlow and Cloud AutoML to run your deep learning models
Book Description
With a wide range of exciting tools and libraries such as Google BigQuery, Google Cloud Dataflow, and Google Cloud Dataproc, Google Cloud Platform (GCP) enables efficient big data processing and the development of smart AI models on the cloud. This GCP book will guide you in using these tools to build your AI-powered applications with ease and managing thousands of AI implementations on the cloud to help save you time.
Starting with a brief overview of Cloud AI and GCP features, you'll learn how to deal with large volumes of data using auto-scaling features. You'll then implement Cloud AutoML to demonstrate the use of streaming components for performing data analytics and understand how Dialogflow can be used to create a conversational interface. As you advance, you'll be able to scale out and speed up AI and predictive applications using TensorFlow. You'll also leverage GCP to train and optimize deep learning models, run machine learning algorithms, and perform complex GPU computations using TPUs. Finally, you'll build and deploy AI applications to production with the help of an end-to-end use case.
By the end of this book, you'll have learned how to design and run experiments and be able to discover innovative solutions without worrying about infrastructure, resources, and computing power.
What you will learn
Understand the basics of cloud computing and explore GCP components
Work with the data ingestion and preprocessing techniques in GCP for machine learning
Implement machine learning algorithms with Google Cloud AutoML
Optimize TensorFlow machine learning with Google Cloud TPUs
Get to grips with operationalizing AI on GCP
Build an end-to-end machine learning pipeline using Cloud Storage, Cloud Dataflow, and Cloud Datalab
Build models from petabytes of structured and semi-structured data using BigQuery ML
Who this book is for
If you're an artificial intelligence developer, data scientist, machine learning engineer, or deep learning engineer looking to build and deploy smart applications on Google Cloud Platform, you'll find this book useful. A fundamental understanding of basic data processing and machine learning concepts is necessary. Though not mandatory, familiarity with Google Cloud Platform will help you make the most of this book.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Dimensions
Height: 93 mm
Width: 75 mm
ISBN-13
978-1-78953-846-5 (9781789538465)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Anand Deshpande has over 19 years' experience with IT services and product development. He is currently working as Vice President of Advanced Analytics and Product Development at VSquare Systems Pvt. Ltd. (VSquare). He has developed a special interest in data science and an algorithmic approach to data management and analytics and co-authored a book entitled Artificial Intelligence for Big Data in May 2018.
Manish Kumar works as Director of Technology and Architecture at VSquare. He has over 13 years' experience in providing technology solutions to complex business problems. He has worked extensively on web application development, IoT, big data, cloud technologies, and blockchain. Aside from this book, Manish has co-authored three books (Mastering Hadoop 3, Artificial Intelligence for Big Data, and Building Streaming Applications with Apache Kafka).
Vikram Chaudhari works as Director of Data and Advanced Analytics at VSquare. He has over 10 years' IT experience. He is a certified AWS and Google Cloud Architect and has completed multiple implementations of data pipelines with Amazon Web Services and Google Cloud Platform. With implementation experience on multiple data pipelines across platforms, Vikram is instrumental in creating reusable components and accelerators that reduce costs and implementation time.
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Manish Kumar works as Director of Technology and Architecture at VSquare. He has over 13 years' experience in providing technology solutions to complex business problems. He has worked extensively on web application development, IoT, big data, cloud technologies, and blockchain. Aside from this book, Manish has co-authored three books (Mastering Hadoop 3, Artificial Intelligence for Big Data, and Building Streaming Applications with Apache Kafka).
Vikram Chaudhari works as Director of Data and Advanced Analytics at VSquare. He has over 10 years' IT experience. He is a certified AWS and Google Cloud Architect and has completed multiple implementations of data pipelines with Amazon Web Services and Google Cloud Platform. With implementation experience on multiple data pipelines across platforms, Vikram is instrumental in creating reusable components and accelerators that reduce costs and implementation time.
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Content
Table of Contents
Overview of Artificial Intelligence and Google Cloud Platform
Computing and Processing Using GCP Components
Building Machine Learning Applications with XGBoost
Using Cloud AutoML
Building a Big Data Cloud Machine Learning Engine
Building Smart Conversational Applications Using DialogFlow
Understanding Cloud Tensor Processing Units
Implement TensorFlow models using Cloud Machine Learning Engine
Building Prediction Applications using Tensorflow Models
Building an Artificial Intelligence application
Overview of Artificial Intelligence and Google Cloud Platform
Computing and Processing Using GCP Components
Building Machine Learning Applications with XGBoost
Using Cloud AutoML
Building a Big Data Cloud Machine Learning Engine
Building Smart Conversational Applications Using DialogFlow
Understanding Cloud Tensor Processing Units
Implement TensorFlow models using Cloud Machine Learning Engine
Building Prediction Applications using Tensorflow Models
Building an Artificial Intelligence application