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Google Cloud Platform for Architects

You're reading from   Google Cloud Platform for Architects Design and manage powerful cloud solutions

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788834308
Length 372 pages
Edition 1st Edition
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Authors (3):
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Loonycorn Ravi Loonycorn Ravi
Author Profile Icon Loonycorn Ravi
Loonycorn Ravi
Judy Raj Judy Raj
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Judy Raj
Vitthal Srinivasan Vitthal Srinivasan
Author Profile Icon Vitthal Srinivasan
Vitthal Srinivasan
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Table of Contents (19) Chapters Close

Preface 1. The Case for Cloud Computing FREE CHAPTER 2. Introduction to Google Cloud Platform 3. Compute Choices – VMs and the Google Compute Engine 4. GKE, App Engine, and Cloud Functions 5. Google Cloud Storage – Fishing in a Bucket 6. Relational Databases 7. NoSQL Databases 8. BigQuery 9. Identity and Access Management 10. Managing Hadoop with Dataproc 11. Load Balancing 12. Networking in GCP 13. Logging and Monitoring 14. Infrastructure Automation 15. Security on the GCP 16. Pricing Considerations 17. Effective Use of the GCP 18. Other Books You May Enjoy

Cloud ML Engine

The Cloud ML Engine offers two distinct subservices: distributed training and distributed prediction. Training refers to the process of building a machine learning model from a corpus of data, whereas prediction refers to the use of that model to actually make forecasts.

Depending on how long your ML training jobs run and whether you use batch or online mode for prediction, the costs vary significantly. A fairly heavy-duty set of ML workloads will cost about $4,000 per month:

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