Book Image

Cloud Analytics with Google Cloud Platform

By : Sanket Thodge
Book Image

Cloud Analytics with Google Cloud Platform

By: Sanket Thodge

Overview of this book

With the ongoing data explosion, more and more organizations all over the world are slowly migrating their infrastructure to the cloud. These cloud platforms also provide their distinct analytics services to help you get faster insights from your data. This book will give you an introduction to the concept of analytics on the cloud, and the different cloud services popularly used for processing and analyzing data. If you’re planning to adopt the cloud analytics model for your business, this book will help you understand the design and business considerations to be kept in mind, and choose the best tools and alternatives for analytics, based on your requirements. The chapters in this book will take you through the 70+ services available in Google Cloud Platform and their implementation for practical purposes. From ingestion to processing your data, this book contains best practices on building an end-to-end analytics pipeline on the cloud by leveraging popular concepts such as machine learning and deep learning. By the end of this book, you will have a better understanding of cloud analytics as a concept as well as a practical know-how of its implementation
Table of Contents (16 chapters)
Title Page
Packt Upsell
Foreword
Contributors
Preface
Index

TensorFlow


TensorFlow is an open source software library for numerical computation using data flow graphs. Deep learning in the cloud platform is a new type of service facilitated by all Cloud platforms nowadays. This service can use TensorFlow in Google Cloud Platform, which enables users to develop a quick and easy way of application design, development and deployment. TensorFlow for deep learning research and development applications is used in many domains such as natural language processing, speech recognition and translation, and computer vision and so on. TensorFlow is also used to recommend the user to do quick decision making.

The architecture of the TensorFlow model is as follows:

The following components are used in the TensorFlow model:

  • Cloud TensorFlow
  • Cloud Pub/Sub
  • Cloud Dataflow
  • Cloud Bigtable
  • Cloud BigQuery
  • Google Data Studio
  • Apigee API Platform
  • Cloud Endpoints

The applications of this model include:

  • Automotive
  • Statistics
  • Sentiment analysis in CRM
  • Security
  • Flaw detection
  • Recommendation systems...