Hands-On Artificial Intelligence on Google Cloud Platform (ePub)

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.

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.