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Hands-On Machine Learning on Google Cloud Platform
Hands-On Machine Learning on Google Cloud Platform

Hands-On Machine Learning on Google Cloud Platform: Implementing smart and efficient analytics using Cloud ML Engine

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Profile Icon Perrier Profile Icon V Kishore Ayyadevara Profile Icon Giuseppe Ciaburro
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Profile Icon Perrier Profile Icon V Kishore Ayyadevara Profile Icon Giuseppe Ciaburro
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5 (2 Ratings)
Paperback Apr 2018 500 pages 1st Edition
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Hands-On Machine Learning on Google Cloud Platform

Google Compute Engine

The core service of Google Cloud Platform (GCP) is Google Compute Engine (GCE). The GCE allows you to launch spin up virtual machines (VMs) with the right operating system, size, RAM, and appropriate number of CPUs or GPUs for your needs. It is an equivalent of AWS EC2. With GCE, we dive into the core of GCP.

In this chapter, you will learn how to:

  • Create VM instances on GCE that are adapted to your projects.
  • Use Google's command-line tools to manage your VMs.
  • Set up a Python data science stack on a GCE VM with conda and scikit-learn.
  • Access your VM via a password-protected Jupyter Notebook. And we'll cover more advanced topics related to images, snapshots, pre-emptibles VMs, startup script, and IPs.

By the end of this chapter, you will be able to create and fully manage your VM both via the online console and the command-line tools, as well as...

Google Compute Engine

Simply put, GCE is a service that lets you create and run VMs on Google infrastructure. The GCE allows you to launch spin up VMs with the right operating system, size, RAM, and the appropriate number of CPUs or GPUs for your needs. It is the equivalent of AWS EC2.

The GCE was announced on June 28, 2012, at Google I/O 2012 and made available to the general public on May 15, 2013. Compared to AWS EC2, an equivalent product, the GCE is a rather new service:

The following extracts from the release notes timeline illustrate the rapid evolution of the GCE service from a simple contender to a fully fledged player in the Cloud computing domain:

  • May 15, 2013: GCE is available for everyone.
  • August 6, 2013: GCE launches load balancing.
  • December 3, 2013: GCE is announced as being production ready. Users can now feel confident using Compute Engine to support mission...

Setting up a data science stack on the VM

So, now we have a VM running and we're able to send files to it, connect to it, and modify it. Everything is ready for us to set it up for data science!

We will install the Python Miniconda stack from continuum, much smaller than the full Conda distribution. Do SSH into your instance.

  1. Install the mini sudo apt-get update sudo apt-get install bzip2 wget from https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh using the following command:
bash Miniconda2-latest-Linux-x86_64.sh
  1. And then install the Python stack with conda:
$ conda install scikit-learn pandas jupyter ipython

Don't forget to do this:

$ source .bashrc

BOX the ipython console

To launch a Jupyter Notebook in your instance and access it over the Web, you need to promote the ephemeral external IP address provided by default to your VM to a static external IP.

You also need to make sure that your instance is accepting HTTP and HTTPS traffic. For that, go to your VM page, edit it, and check the following checkboxes:

Since your Jupyter Notebook is open to all traffic on the web, you password-protect it:

  1. Generate a configuration:

$ jupyter notebook --generate-config
  1. And add a password with:
$ jupyter notebook password

More info on how to protect your public Notebook, including using ssh keys and adding encryption, is available at http://jupyter-notebook.readthedocs.io/en/latest/public_server.html.

  1. Launch your Jupyter Notebook with:
$ jupyter notebook --ip=0.0.0.0 --port=8888 --no-browser &

This generates a token and the...

Resources and further reading

The following are a few interesting articles on setting TensorFlow on GCE:

Some docker-related resources:

Summary

The GCE is a GCP core service, offering a wide variety of scalable VMs based on different OSes. The multiplicity of available OSes, the range of machines with CPUs, GPUs, small to huge disk space, and RAM make the GCE a powerful Cloud environment adapted to a wide variety of projects and contexts.

In this chapter you learned:

  • How to create, launch, back up, modify, and access multiple VMs
  • The different parameters and variables related to a VM
  • How to access and use the Google Shell
  • How to use the gcloud CLI to carry out the same operations in the GCP
  • How to install a data science Python stack
  • How to launch a Jupyter Notebook

There are many more possibilities offered by the power and flexibility of the GCE that we haven't covered. Hopefully, by the end of this chapter, you should feel comfortable working with instances that are appropriate for your projects in a data...

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Key benefits

  • Get well versed in GCP pre-existing services to build your own smart models
  • A comprehensive guide covering aspects from data processing, analyzing to building and training ML models
  • A practical approach to produce your trained ML models and port them to your mobile for easy access

Description

Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems.

Who is this book for?

This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy

What you will learn

  • Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile
  • Create, train and optimize deep learning models for various data science problems on big data
  • Learn how to leverage BigQuery to explore big datasets
  • Use Google's pre-trained TensorFlow models for NLP, image, video and much more
  • Create models and architectures for Time series, Reinforcement Learning, and generative models
  • Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 30, 2018
Length: 500 pages
Edition : 1st
Language : English
ISBN-13 : 9781788393485
Vendor :
Google
Category :

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Product Details

Publication date : Apr 30, 2018
Length: 500 pages
Edition : 1st
Language : English
ISBN-13 : 9781788393485
Vendor :
Google
Category :

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Table of Contents

17 Chapters
Introducing the Google Cloud Platform Chevron down icon Chevron up icon
Google Compute Engine Chevron down icon Chevron up icon
Google Cloud Storage Chevron down icon Chevron up icon
Querying Your Data with BigQuery Chevron down icon Chevron up icon
Transforming Your Data Chevron down icon Chevron up icon
Essential Machine Learning Chevron down icon Chevron up icon
Google Machine Learning APIs Chevron down icon Chevron up icon
Creating ML Applications with Firebase Chevron down icon Chevron up icon
Neural Networks with TensorFlow and Keras Chevron down icon Chevron up icon
Evaluating Results with TensorBoard Chevron down icon Chevron up icon
Optimizing the Model through Hyperparameter Tuning Chevron down icon Chevron up icon
Preventing Overfitting with Regularization Chevron down icon Chevron up icon
Beyond Feedforward Networks – CNN and RNN Chevron down icon Chevron up icon
Time Series with LSTMs Chevron down icon Chevron up icon
Reinforcement Learning Chevron down icon Chevron up icon
Generative Neural Networks Chevron down icon Chevron up icon
Chatbots Chevron down icon Chevron up icon

Customer reviews

Rating distribution
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Dan Jun 04, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Many different AI topics are covered (ANN, CNN, RNN) and technical details and examples of different approaches are provided.There is a feeling that multiple authors worked on the book as changes of grammar are detectable as well as sometimes repeated examples/notes, that the book was "assembled".Finally, the last chapter of Chatbot was likely thrown in as a marketing afterthought as it is a hot topic. Unlike the rest of the book, the chapter of Chatbot is very empty, nothing that I could not already find on a free 2 minute YouTube video.
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JM Dec 05, 2018
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Really nice, good price
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