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Cloud Analytics with Google Cloud Platform

You're reading from   Cloud Analytics with Google Cloud Platform An end-to-end guide to processing and analyzing big data using Google Cloud Platform

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788839686
Length 282 pages
Edition 1st Edition
Concepts
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Author (1):
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Sanket Thodge Sanket Thodge
Author Profile Icon Sanket Thodge
Sanket Thodge
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Table of Contents (11) Chapters Close

Preface 1. Introducing Cloud Analytics 2. Design and Business Considerations FREE CHAPTER 3. GCP 10,000 Feet Above – A High-Level Understanding of GCP 4. Ingestion and Storing – Bring the Data and Capture It 5. Processing and Visualizing – Close Encounter 6. Machine Learning, Deep Learning, and AI on GCP 7. Guidance on Google Cloud Platform Certification 8. Business Use Cases 9. Introduction to AWS and Azure 10. Other Books You May Enjoy

Google Cloud Machine Learning Engine


This is an API that creates a model in machine learning, and can work on any size and any type of data. A major use of this ML is to train a model and predict from it. The ML engine can use any model to perform large-scale analysis on a cluster for managing online and batch programming. It can support a few thousand users and performs on terabytes of data. This service can easily be combined with other services such as Dataflow, Storage, BigQuery, and so on provided by GCP. The ML Engine lets users build models using DataLab and can also build portable models that work on various devices.

The purpose of Cloud ML Engine is to train a new ML model at scale using the TensorFlow application, and the model is hosted to get predictions on a new set of data.

The ML Engine Workflow can be formatted into the following steps:

  1. Evaluating the problem
  2. Data exploration and preparation
  3. Model development and training
  4. Model testing and deployment
  5. Operational development and...
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