Book Image

Google Cloud Platform for Architects

By : Vitthal Srinivasan, Loonycorn , Judy Raj
Book Image

Google Cloud Platform for Architects

By: Vitthal Srinivasan, Loonycorn , Judy Raj

Overview of this book

Using a public cloud platform was considered risky a decade ago, and unconventional even just a few years ago. Today, however, use of the public cloud is completely mainstream - the norm, rather than the exception. Several leading technology firms, including Google, have built sophisticated cloud platforms, and are locked in a fierce competition for market share. The main goal of this book is to enable you to get the best out of the GCP, and to use it with confidence and competence. You will learn why cloud architectures take the forms that they do, and this will help you become a skilled high-level cloud architect. You will also learn how individual cloud services are configured and used, so that you are never intimidated at having to build it yourself. You will also learn the right way and the right situation in which to use the important GCP services. By the end of this book, you will be able to make the most out of Google Cloud Platform design.
Table of Contents (19 chapters)
13
Logging and Monitoring

Understand the main choices for ML applications

We have not spent a lot of time discussing machine learning on the GCP in this book, but at a very high level, you have two choices:

  • TensorFlow and the Cloud ML Engine
  • SparkML and Dataproc

Both options are good. The Cloud ML Engine has support for distributed training and prediction and is tightly coupled with TensorFlow, which is a great technology for deep learning. So, this option is probably a better one, on balance.

SparkML is a great option too, though. Spark is possibly the hottest big data technology today; therefore, there are a lot of existing Spark applications and a lot of talented Spark developers out there today. If your organization uses a lot of Spark right now, you might find the SparkML on Dataproc option to be a better one, at least until TensorFlow and the ML Engine catch on in popularity in your firm.

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