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

Hands-On Machine Learning on Google Cloud Platform

Implementing smart and efficient analytics using Cloud ML Engine

Giuseppe Ciaburro

V Kishore Ayyadevara

Alexis Perrier

BIRMINGHAM - MUMBAI

Hands-On Machine Learning on Google Cloud Platform

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Contributors

About the authors

Giuseppe Ciaburro holds a PhD in environmental technical physics and two master's degrees. His research is on machine learning applications in the study of urban sound environments. He works at Built Environment Control Laboratory, Università degli Studi della Campania Luigi Vanvitelli (Italy). He has over 15 years' experience in programming Python, R, and MATLAB, first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit.

V Kishore Ayyadevara has over 9 years' experience of using analytics to solve business problems and setting up analytical work streams through his work at American Express, Amazon, and, more recently, a retail analytics consulting startup. He has an MBA from IIM Calcutta and is also an electronics and communications engineer. He has worked in credit risk analytics, supply chain analytics, and consulting for multiple FMCG companies to identify ways to improve their profitability.

Alexis Perrier is a data science consultant with experience in signal processing and stochastic algorithms. He holds a master's in mathematics from Université Pierre et Marie Curie Paris VI and a PhD in signal processing from Télécom ParisTech. He is actively involved in the DC data science community. He is also an avid book lover and proud owner of a real chalk blackboard, where he regularly shares his fascination of mathematical equations with his kids.

About the reviewers

Mikhail Berlyant is a data warehousing veteran. He has been a data developer since the late 1970s. Since 2000, he has led data systems, data mining, and data warehouse teams at Yahoo! and Myspace.

He is a Google Cloud expert and senior VP of Technology, at Viant Inc., a people-based advertising tech company that enables marketers to plan, execute, and measure their digital media investments through a cloud-based platform. At Viant, he led the migration of a petabyte-sized data warehouse to Google Cloud. He is currently focusing on self-serve/productivity tools for BigQuery/GCP.

I'd like to say thanks to my beautiful wife, Svetlana, for supporting me in all my endeavors.

Sanket Thodge is an entrepreneur by profession in Pune, India. He is an author of Cloud Analytics with Google Cloud Platform. He founded Pi R Square Digital Solutions. With expertise as a Hadoop developer, he has explored the cloud, IoT, machine learning, and blockchain. He has also applied for a patent in IoT and has worked with numerous startups and MNCs, providing consultancy, architecture building, development, and corporate training across the globe.

Antonio Gulli is a transformational software executive and business leader with a passion for establishing and managing global technological talent for innovation and execution. He is an expert in search engines, online services, machine learning, and so on. Currently, he is a site lead and director of cloud at Google Warsaw, driving European efforts for serverless, Kubernetes, and Google Cloud UX. Antonio has filed for 20+ patents, published multiple academic papers, and served as a senior PC member in multiple international conferences.

Chirag Nayyar is helping organizations to migrate their workload from on-premise to the public cloud. He has experience in web app migration, SAP workload on the cloud, and EDW. He is currently working at Cloud Kinetics Technology Solutions. He holds a wide range of certifications from all major public cloud platforms. He also runs meetups and is a regular speaker at various cloud events.

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Table of Contents

Title Page

Copyright and Credits

Hands-On Machine Learning on Google Cloud Platform

Packt Upsell

Why subscribe?

PacktPub.com

Contributors

About the authors

About the reviewers

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Introducing the Google Cloud Platform

ML and the cloud

The nature of the cloud

Public cloud

Managed cloud versus unmanaged cloud

IaaS versus PaaS versus SaaS

Costs and pricing

ML

Introducing the GCP

Mapping the GCP

Getting started with GCP

Project-based organization

Creating your first project

Roles and permissions

Further reading

Summary

Google Compute Engine

Google Compute Engine

VMs, disks, images, and snapshots

Creating a VM

Google Shell

Google Cloud Platform SDK

Gcloud

Gcloud config

Accessing your instance with gcloud

Transferring files with gcloud

Managing the VM

IPs

Setting up a data science stack on the VM

BOX the ipython console

Troubleshooting

Adding GPUs to instances

Startup scripts and stop scripts

Resources and further reading

Summary

Google Cloud Storage

Google Cloud Storage

Box–storage versus drive

Accessing control lists

Access and management through the web console

gsutil

gsutil cheatsheet

Advanced gsutil

Signed URLs

Creating a bucket in Google Cloud Storage

Google Storage namespace

Naming a bucket

Naming an object

Creating a bucket

Google Cloud Storage console

Google Cloud Storage gsutil

Life cycle management

Google Cloud SQL

Databases supported

Google Cloud SQL performance and scalability

Google Cloud SQL security and architecture

Creating Google Cloud SQL instances

Summary

Querying Your Data with BigQuery

Approaching big data

Data structuring

Querying the database

SQL basics

Google BigQuery

BigQuery basics

Using a graphical web UI

Visualizing data with Google Data Studio

Creating reports in Data Studio

Summary

Transforming Your Data

How to clean and prepare the data

Google Cloud Dataprep

Exploring Dataprep console

Removing empty cells

Replacing incorrect values

Mismatched values

Finding outliers in the data

Visual functionality

Statistical information

Removing outliers

Run Job

Scale of features

Min–max normalization

z score standardization

Google Cloud Dataflow

Summary

Essential Machine Learning

Applications of machine learning

Financial services

Retail industry

Telecom industry

Supervised and unsupervised machine learning

Overview of machine learning techniques

Objective function in regression

Linear regression

Decision tree

Random forest

Gradient boosting

Neural network

Logistic regression

Objective function in classification

Data splitting

Measuring the accuracy of a model

Absolute error

Root mean square error

The difference between machine learning and deep learning

Applications of deep learning

Summary

Google Machine Learning APIs

Vision API

Enabling the API

Opening an instance

Creating an instance using Cloud Shell

Label detection

Text detection

Logo detection

Landmark detection

Cloud Translation API

Enabling the API

Natural Language API

Speech-to-text API

Video Intelligence API

Summary

Creating ML Applications with Firebase

Features of Firebase

Building a web application

Building a mobile application

Summary

Neural Networks with TensorFlow and Keras

Overview of a neural network

Setting up Google Cloud Datalab

Installing and importing the required packages

Working details of a simple neural network

Backpropagation

Implementing a simple neural network in Keras

Understanding the various loss functions

Softmax activation

Building a more complex network in Keras

Activation functions

Optimizers

Increasing the depth of network

Impact on change in batch size

Implementing neural networks in TensorFlow

Using premade estimators

Creating custom estimators

Summary

Evaluating Results with TensorBoard

Setting up TensorBoard

Overview of summary operations

Ways to debug the code

Setting up TensorBoard from TensorFlow

Summaries from custom estimator

Summary

Optimizing the Model through Hyperparameter Tuning

The intuition of hyperparameter tuning

Overview of hyperparameter tuning

Hyperparameter tuning in Google Cloud

The model file

Configuration file

Setup file

The __init__ file

Summary

Preventing Overfitting with Regularization

Intuition of over/under fitting

Reducing overfitting

Implementing L2 regularization

Implementing L1 regularization

Implementing dropout

Reducing underfitting

Summary

Beyond Feedforward Networks – CNN and RNN

Convolutional neural networks

Convolution layer

Rectified Linear Units

Pooling layers

Fully connected layer

Structure of a CNN

TensorFlow overview

Handwriting Recognition using CNN and TensorFlow

Run Python code on Google Cloud Shell

Recurrent neural network

Fully recurrent neural networks

Recursive neural networks

Hopfield recurrent neural networks

Elman neural networks

Long short-term memory networks

Handwriting Recognition using RNN and TensorFlow

LSTM on Google Cloud Shell

Summary

Time Series with LSTMs

Introducing time series 

Classical approach to time series

Estimation of the trend component

Estimating the seasonality component

Time series models

Autoregressive models

Moving average models

Autoregressive moving average model 

Autoregressive integrated moving average models

Removing seasonality from a time series

Analyzing a time series dataset

Identifying a trend in a time series

Time series decomposition

Additive method

Multiplicative method

LSTM for time series analysis

Overview of the time series dataset

Data scaling

Data splitting

Building the model

Making predictions

Summary

Reinforcement Learning

Reinforcement learning introduction

Agent-Environment interface

Markov Decision Process

Discounted cumulative reward

Exploration versus exploitation

Reinforcement learning techniques

Q-learning

Temporal difference learning

Dynamic Programming

Monte Carlo methods

Deep Q-Network

OpenAI Gym

Cart-Pole system

Learning phase

Testing phase

Summary

Generative Neural Networks

Unsupervised learning

Generative models

Restricted Boltzmann machine

Boltzmann machine architecture

Boltzmann machine disadvantages

Deep Boltzmann machines

Autoencoder

Variational autoencoder

Generative adversarial network

Adversarial autoencoder 

Feature extraction using RBM

Breast cancer dataset

Data preparation

Model fitting

Autoencoder with Keras

Load data

Keras model overview

Sequential model

Keras functional API

Define model architecture

Magenta

The NSynth dataset

Summary

Chatbots

Chatbots fundamentals

Chatbot history

The imitation game

Eliza

Parry

Jabberwacky

Dr. Sbaitso

ALICE

SmarterChild

IBM Watson

Building a bot

Intents

Entities

Context

Chatbots

Essential requirements

The importance of the text

Word transposition

Checking a value against a pattern

Maintaining context

Chatbots architecture

Natural language processing

Natural language understanding

Google Cloud Dialogflow

Dialogflow overview

Basics Dialogflow elements

Agents

Intent

Entity

Action

Context

Building a chatbot with Dialogflow

Agent creation

Intent definition

Summary

Preface

Google Cloud ML Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn how to build and train different complexities of machine learning models at scale, but also to 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 how to create powerful machine-learning-based applications from scratch 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 others. We will implement a wide variety of deep learning use cases and will 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 be aware of the main difficulties that you may encounter, and be familiar with appropriate strategies to overcome these difficulties and build efficient systems.

Who this book is 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 interaction with the Google ML platform is mostly done via the command line, the reader should have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will also be handy.

What this book covers

Chapter 1, Introducing the Google Cloud Platform, explores different services that may be useful to build a machine learning pipeline based on GCP.

Chapter 2, Google Compute Engine, helps you to create and fully manage your VM via both the online console and command-line tools, as well as how to implement a data science workflow and a Jupyter Notebook workspace.

Chapter 3, Google Cloud Storage, shows how to upload data and manage it using the services provided by the Google Cloud Platform.

Chapter 4, Querying Your Data with BigQuery, shows you how to query data from Google Storage and visualize it with Google Data Studio.

Chapter 5, Transforming Your Data, presents Dataprep, a service useful for preprocessing data, extracting features, and cleaning up records. We also look at Dataflow, a service used to implement streaming and batch processing.

Chapter 6, Essential Machine Learning, starts our journey into machine learning and deep learning; we learn when to apply each one.

Chapter 7, Google Machine Learning APIs, teaches us how to use Google Cloud machine learning APIs for image analysis, text and speech processing, translation, and video inference.

Chapter 8, Creating ML Applications with Firebase, shows how to integrate different GCP services to build a seamless machine-learning-based application, mobile or web-based.

Chapter 9, Neural Networks with TensorFlow and Keras, gives a good understanding of the structure and key elements of a feedforward network, how to architecture one, and how to tinker and experiment with different parameters.

Chapter 10, Evaluating Results with TensorBoard, shows how the choice of different parameters and functions impacts the performance of the model.

Chapter 11, Optimizing the Model through Hyperparameter Tuning, teaches us how to use hypertuning in TensorFlow application code and interpret the results to select the best performing model.

Chapter 12, Preventing Overfitting with Regularization, shows how to identify overfitting and make our models more robust to previously unseen data by setting the right parameters and defining the proper architectures.

Chapter 13, Beyond Feedforward Networks – CNN and RNNs, teaches which type of neural network to apply to different problems, and how to define and implement them on GCP.

Chapter 14, Time Series with LSTMs, shows how to create LSTMs and apply them to time series predictions. We will also understand when LSTMs outperform more standard approaches.

Chapter 15, Reinforcement Learning, introduces the power of reinforcement learning and shows how to implement a simple use case on GCP.

Chapter 16, Generative Neural Networks, teaches us how to extract the content generated within the neural net with different types of content—text, images, and sounds.

Chapter 17, Chatbots, shows how to train a contextual chatbot while implementing it in a real mobile application.

To get the most out of this book

In this book, machine learning algorithms are implemented on the Google Cloud Platform. To reproduce the many examples in this book, you need to possess a working account on GCP. We have used Python 2.7 and above to build various applications. In that spirit, we have tried to keep all of the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.

Download the example code files

You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

Log in or register at www.packtpub.com.

Select the SUPPORT tab.

Click on Code Downloads & Errata.

Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

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7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Machine-Learning-on-Google-Cloud-Platform. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/HandsOnMachineLearningonGoogleCloudPlatform_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: Where GROUP is a service or an account element and COMMAND is the command to send to the GROUP.

A block of code is set as follows:

import matplotlib.patches as patches

import numpy as np

fig,ax = plt.subplots(1)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

text=this is a good text

from google.cloud.language_v1 import types

document = types.Document(

        content=text,

        type='PLAIN_TEXT')

Any command-line input or output is written as follows:

$ gcloud compute instances list

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: Click on Create a new project.

Warnings or important notes appear like this.

Tips and tricks appear like this.

Get in touch

Feedback from our readers is always welcome.

General feedback: Email [email protected] and mention the book title in the subject of your message. If you have questions about any aspect of this book, please email us at [email protected].

Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit www.packtpub.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details.

Piracy: If you come across any illegal copies of our works in any form on the Internet, we would be grateful if you would provide us with the location address or website name. Please contact us at [email protected] with a link to the material.

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Reviews

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Introducing the Google Cloud Platform

The goal of this first introductory chapter is to give you an overview of the Google Cloud Platform (GCP). We start by explaining why machine learning (ML) and cloud computing go hand in hand as the demand for ever more hungry computing resources grows for today's ML applications. We then proceed with a 360° presentation of the platform's data-related services. Account and project creation as well as role allocation close the chapter.

A data science project follows a regular set of steps: in extracting the data, exploring, cleaning it, extracting information, training and assessing models, and finally building machine-learning-enabled applications. For each step of the data science flow, there are one or several services in the GCP that are adequate.

But, before we present the overall mapping of the GCP data-related services, it is important to understand why ML and cloud computing are truly made for each other.

In this chapter, we will cover the following topics:

ML and the cloud

Introducing the GCP

Data services of the Google platform

ML and the cloud

In short, artificial intelligence (AI) requires a lot of computing resources. Cloud computing addresses those concerns.

ML is a new type of microscope and telescope, allowing each of to us to push the boundaries of human knowledge and human activities. With ever more powerful ML platforms and open tools, we are able to conquer new realms of knowledge and grow new types of businesses. From the comfort of our laptops, at home, or at the office, we can better understand and predict human behavior in a wide range of domains. Think health care, transportation, energy, financial markets, human communication, human-machine interaction, social network dynamics, economic behavior, and nature (astronomy, global warming, or seismic activity). The list of domains affected by the explosion of AI is truly unlimited. The impact on society? Astounding.

With so many resources available to anyone with an online connection, the barrier to joining the AI revolution has never been lower than it is now. Books, tutorials, MOOCs, and meet-ups, as well as open source libraries in a myriad of languages, are freely available to both the seasoned and the beginner data scientist.

As veteran data scientists know well, data science is always hungry for more computational resources. Classification on the Iris or the MINST image datasets or predictive modeling on Titanic passengers does not reflect real-world data. Real-world data is by essence dirty, incomplete, noisy, multi-sourced, and more often than not, in large volumes. Exploiting these large datasets requires computational power, storage, CPUs, GPUs, and fast I/O.

However, more powerful machines are not sufficient to build meaningful ML applications. Grounded in science, data science requires a scientific mindset with concepts such as reproducibility and reviewing. Both aspects are made easier by working with online accessible resources. Sharing datasets and models and exposing results is always more difficult when the data lives on one person's computer. Reproducing results and maintaining models with new data also requires easy accessibility to assets. And as we work on ever more personalized and critical data (for instance in healthcare), privacy and security concerns become all the more important to the project stakeholders.

This is where the cloud comes in, by offering scalability and accessibility while providing an adequate level of security.

Before diving into GCP, let's learn a bit more about the cloud.

The nature of the cloud

ML projects are resource intensive. From storage to computational power, training models sometimes require resources that cannot be found on a simple standalone computer. Physical limitations in terms of storage have shrunk in recent years. As we now enjoy reliable terabyte storage accessible at reduced prices, storage is no longer an issue for most data projects that are not in the realm of big data. Computing power has also increased so much that what required expensive workstations a few years ago can now run on laptops.

However, despite all this amazingly rapid evolution, the power of the standalone PC is finite. There is an upper limit to the volume of data you can store on your machine and to the time you're willing to wait to get your model trained. New frontiers in AI, with speech-to-text, video captioning in real time, self-driving cars, music generation, or chatbots that can fool a human being and pass the turing test, require ever larger resources. This is especially true of deep learning models, which are too slow on standard CPUs and require GPU-based machines to train in a reasonable amount of time.

ML in the cloud does not face these limitations. What you get with cloud computing is direct access to high-performance computing (HPC). Before the cloud (roughly before AWS launched its Elastic Computing Cloud (EC2) service in 2006), HPC was only available via supercomputers, such as the Cray computers. Cray is a US company that has built some of the most powerful supercomputers since the 1960s. China's Tianhe-2 is now the most powerful supercomputer in the world, with a capacity of 100,000 petaflops (that's 10² x 10¹⁵, or 10 to the power of 17 floating-point operations per second!).

A supercomputer not only costs millions of US dollars but also requires its own physical infrastructure and has huge maintenance costs. It is also out of reach for individuals and for most companies. Engineers and researchers, hungry for HPC, now turn to on-demand cloud infrastructures. Cloud service offers are democratizing access to HPC.

Computing in the cloud is built on a distributed architecture. The processors are distributed across different servers instead of being aggregated in one single machine. With a few clicks or command lines, anyone can sign up massively complex banks of servers in a matter of minutes. The amount of power at your command can be mind-blowing.

Cloud computing can not only handle the most demanding optimization tasks but also carry out a simple regression on a tiny dataset. Cloud computing is extremely flexible.

To recap, cloud computing offers:

Instantaneity: Resources can be made available in a matter of minutes.

On-demand: Instances can be put on stand by or decommissioned when no longer needed.

Diversity: The wide range of operating systems, storage, and database solutions, allow the architect to create project-focused architectures, from simple mobile applications to ML APIs.

Unlimited resources: If not infinite yet, the volume of resources for storage computing and networks you can assemble is mind-blowing.

GPUs: Most PCs are based on CPUs (with the exception of machines optimized for gaming). Deep learning requires GPUs to achieve human-compatible speeds for training models. Cloud computing makes GPUs available at a fraction of the cost needed to buy GPU machines.

Controlled accessibility and security: With granular role definitions, service compartmentalization, encrypted connections, and user-based access control, cloud platforms greatly reduce the risk of intrusion and data loss.

Apart from these, there are several other types of cloud platforms and offers on the market.

Public cloud

There are two main types of cloud models depending on the needs of the customers: public versus private and multi-tenant versus single-tenant. These different cloud types offer different levels of management, security, and pricing.

A public cloud consists of resources that are located off-site over the internet. In a public cloud, the infrastructure is typically multi-tenant. Multiple customers can share the same underlying hardware or server. Resources such as networking, storage, power, cooling and computing are all shared. The customer usually has no visibility of where this infrastructure is hosted except for choosing a geographic region. The pricing mode of a public cloud service is based on the volume of data, the computing power that is used and other infrastructure-management-related services—or, more precisely, a mix of RAM, vCPUs, disk, and bandwidth.

In a private cloud, the resources are dedicated to a single customer; the architecture is single-tenant instead of multi-tenant. The servers are located on premise or in a remote data center. Customers own (or rent) the infrastructure and are responsible for maintaining it. Private cloud infrastructures are more expensive to operate as they require dedicated hardware to be secured for a single tenant. Customers of the private cloud have more control over their infrastructure, and therefore they can achieve their compliance and security requirements.

Hybrid clouds are composed of a mix of public clouds and private ones.

The GCP is a public multi-tenant cloud platform. You share the servers you use with other customers and let Google handle the support, the data centers, and the infrastructure.

Managed cloud versus unmanaged cloud

The cloud market has also diversified into two large segments—managed cloud versus unmanaged cloud.

In an unmanaged cloud platform, the infrastructure is self-served. In case of failure, it is the responsibility of the customer to have some mechanisms in place to restore the operations. Unmanaged cloud requires the customer to have the qualified expertise and resources to build, manage, and maintain cloud instances and infrastructures. Focused on self-serving applications, unmanaged cloud offers do not include support with their basic tiers.

In a managed cloud platform, the provider will support the underlying infrastructure by offering monitoring, troubleshooting, and around-the-clock customer service. Managed cloud brings along qualified expertise and resources to the team right away. For many companies, having a service provider to handle their public cloud can be easier and more cost-effective than hiring their own staff to operate their clouds.

The GCP is a public, multi-tenant, and unmanaged cloud service. So are AWS and Azure. Rackspace, on the other hand, is an example of a managed cloud service company. As an example, Rackspace just started offering managed services for GCP in March 2017.

IaaS versus