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9781119556718

Machine Learning in the Aws Cloud

by
  • ISBN13:

    9781119556718

  • ISBN10:

    1119556716

  • Format: Paperback
  • Copyright: 2019-09-11
  • Publisher: Sybex Inc

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Supplemental Materials

What is included with this book?

Summary

Put the power of AWS Cloud machine learning services to work in your business and commercial applications! 

Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services.

Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You’ll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you’ll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems.

•    Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building

•    Discover common neural network frameworks with Amazon SageMaker

•    Solve computer vision problems with Amazon Rekognition

•    Benefit from illustrations, source code examples, and sidebars in each chapter

The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.

Author Biography

ABOUT THE AUTHOR

ABHISHEK MISHRA has more than 19 years' experience across a broad range of enterprise technologies. He consults as a security and fraud solution architect with Lloyds Banking group PLC in London. He is the author of Amazon Web Services for Mobile Developers.

Table of Contents

Introduction xxiii

Part 1 Fundamentals of Machine Learning 1

Chapter 1 Introduction to Machine Learning 3

What is Machine Learning? 4

Tools Commonly Used by Data Scientists 4

Common Terminology 5

Real-World Applications of Machine Learning 7

Types of Machine Learning Systems 8

Supervised Learning 8

Unsupervised Learning 9

Semi-Supervised Learning 10

Reinforcement Learning 11

Batch Learning 11

Incremental Learning 12

Instance-based Learning 12

Model-based Learning 12

The Traditional Versus the Machine Learning Approach 13

A Rule-based Decision System 14

A Machine Learning–based System 17

Summary 25

Chapter 2 Data Collection and Preprocessing 27

Machine Learning Datasets 27

Scikit-learn Datasets 27

AWS Public Datasets 30

Kaggle.com Datasets 30

UCI Machine Learning Repository 30

Data Preprocessing Techniques 31

Obtaining an Overview of the Data 31

Handling Missing Values 42

Creating New Features 44

Transforming Numeric Features 46

One-Hot Encoding Categorical Features 47

Summary 50

Chapter 3 Data Visualization with Python 51

Introducing Matplotlib 51

Components of a Plot 54

Figure 55

Axes55

Axis 56

Axis Labels 56

Grids 57

Title 57

Common Plots 58

Histograms 58

Bar Chart 62

Grouped Bar Chart 63

Stacked Bar Chart 65

Stacked Percentage Bar Chart 67

Pie Charts 69

Box Plot 71

Scatter Plots 73

Summary 78

Chapter 4 Creating Machine Learning Models with Scikit-learn 79

Introducing Scikit-learn 79

Creating a Training and Test Dataset 80

K-Fold Cross Validation 84

Creating Machine Learning Models 86

Linear Regression 86

Support Vector Machines 92

Logistic Regression 101

Decision Trees 109

Summary 114

Chapter 5 Evaluating Machine Learning Models 115

Evaluating Regression Models 115

RMSE Metric 117

R2 Metric 119

Evaluating Classification Models 119

Binary Classification Models 119

Multi-Class Classification Models 126

Choosing Hyperparameter Values 131

Summary 132

Part 2 Machine Learning with Amazon Web Services 133

Chapter 6 Introduction to Amazon Web Services 135

What is Cloud Computing? 135

Cloud Service Models 136

Cloud Deployment Models 138

The AWS Ecosystem 139

Machine Learning Application Services 140

Machine Learning Platform Services 141

Support Services 142

Sign Up for an AWS Free-Tier Account 142

Step 1: Contact Information 143

Step 2: Payment Information 145

Step 3: Identity Verification 145

Step 4: Support Plan Selection 147

Step 5: Confirmation 148

Summary 148

Chapter 7 AWS Global Infrastructure 151

Regions and Availability Zones 151

Edge Locations 153

Accessing AWS 154

The AWS Management Console 156

Summary 160

Chapter 8 Identity and Access Management 161

Key Concepts 161

Root Account 161

User 162

Identity Federation 162

Group 163

Policy164

Role 164

Common Tasks 165

Creating a User 167

Modifying Permissions Associated with an Existing Group 172

Creating a Role 173

Securing the Root Account with MFA 176

Setting Up an IAM Password Rotation Policy 179

Summary 180

Chapter 9 Amazon S3 181

Key Concepts 181

Bucket 181

Object Key 182

Object Value 182

Version ID 182

Storage Class 182

Costs 183

Subresources 183

Object Metadata 184

Common Tasks 185

Creating a Bucket 185

Uploading an Object 189

Accessing an Object 191

Changing the Storage Class of an Object 195

Deleting an Object 196

Amazon S3 Bucket Versioning 197

Accessing Amazon S3 Using the AWS CLI 199

Summary 200

Chapter 10 Amazon Cognito 201

Key Concepts 201

Authentication 201

Authorization 201

Identity Provider 202

Client 202

OAuth 2.0 202

OpenID Connect 202

Amazon Cognito User Pool 202

Identity Pool 203

Amazon Cognito Federated Identities 203

Common Tasks 204

Creating a User Pool 204

Retrieving the App Client Secret 213

Creating an Identity Pool 214

User Pools or Identity Pools: Which One Should You Use? 218

Summary 219

Chapter 11 Amazon DynamoDB 221

Key Concepts 221

Tables 222

Global Tables 222

Items 222

Attributes 222

Primary Keys 222

Secondary Indexes 223

Queries 223

Scans 223

Read Consistency 224

Read/Write Capacity Modes 224

Common Tasks 225

Creating a Table 225

Adding Items to a Table 228

Creating an Index 231

Performing a Scan 233

Performing a Query 235

Summary 236

Chapter 12 AWS Lambda 237

Common Use Cases for Lambda 237

Key Concepts 238

Supported Languages 238

Lambda Functions 238

Programming Model 239

Execution Environment 243

Service Limitations 244

Pricing and Availability 244

Common Tasks 244

Creating a Simple Python Lambda Function Using the AWS Management Console 244

Testing a Lambda Function Using the AWS Management Console 250

Deleting an AWS Lambda Function Using the AWS Management Console 253

Summary 255

Chapter 13 Amazon Comprehend 257

Key Concepts 257

Natural Language Processing 257

Topic Modeling 259

Language Support 259

Pricing and Availability 259

Text Analysis Using the Amazon Comprehend Management Console 260

Interactive Text Analysis with the AWS CLI 262

Entity Detection with the AWS CLI 263

Key Phrase Detection with the AWS CLI 264

Sentiment Analysis with the AWS CLI 265

Using Amazon Comprehend with AWS Lambda 266

Summary 274

Chapter 14 Amazon Lex 275

Key Concepts 275

Bot 275

Client Application 276

Intent 276

Slot 276

Utterance 277

Programming Model 277

Pricing and Availability 278

Creating an Amazon Lex Bot 278

Creating Amazon DynamoDB Tables 278

Creating AWS Lambda Functions 285

Creating the Chatbot 304

Customizing the AccountOverview Intent 308

Customizing the ViewTransactionList Intent 312

Testing the Chatbot 314

Summary 315

Chapter 15 Amazon Machine Learning 317

Key Concepts 317

Datasources 318

ML Model 318

Regularization 319

Training Parameters 319

Descriptive Statistics 320

Pricing and Availability 321

Creating Datasources 321

Creating the Training Datasource 324

Creating the Test Datasource 330

Viewing Data Insights 332

Creating an ML Model 337

Making Batch Predictions 341

Creating a Real-Time Prediction Endpoint for Your Machine Learning Model 346

Making Predictions Using the AWS CLI 347

Using Real-Time Prediction Endpoints with Your Applications 349

Summary 350

Chapter 16 Amazon SageMaker 353

Key Concepts 353

Programming Model 354

Amazon SageMaker Notebook Instances 354

Training Jobs 354

Prediction Instances 355

Prediction Endpoint and Endpoint Configuration 355

Amazon SageMaker Batch Transform 355

Data Channels 355

Data Sources and Formats 356

Built-in Algorithms 356

Pricing and Availability 357

Creating an Amazon SageMaker Notebook Instance 357

Preparing Test and Training Data 362

Training a Scikit-learn Model on an Amazon SageMaker Notebook Instance 364

Training a Scikit-learn Model on a Dedicated Training Instance 368

Training a Model Using a Built-in Algorithm on a Dedicated Training Instance 379

Summary 384

Chapter 17 Using Google TensorFlow with Amazon SageMaker 387

Introduction to Google TensorFlow 387

Creating a Linear Regression Model with Google TensorFlow 390

Training and Deploying a DNN Classifier Using the TensorFlow Estimators API and Amazon SageMaker 408

Summary 419

Chapter 18 Amazon Rekognition 421

Key Concepts 421

Object Detection 421

Object Location 422

Scene Detection 422

Activity Detection 422

Facial Recognition 422

Face Collection 422

API Sets 422

Non-Storage and Storage-Based Operations 423

Model Versioning 423

Pricing and Availability 423

Analyzing Images Using the Amazon Rekognition Management Console 423

Interactive Image Analysis with the AWS CLI 428

Using Amazon Rekognition with AWS Lambda 433

Creating the Amazon DynamoDB Table 433

Creating the AWS Lambda Function 435

Summary 444

Appendix A Anaconda and Jupyter Notebook Setup 445

Installing the Anaconda Distribution 445

Creating a Conda Python Environment 447

Installing Python Packages 449

Installing Jupyter Notebook 451

Summary 454

Appendix B AWS Resources Needed to Use This Book 455

Creating an IAM User for Development 455

Creating S3 Buckets 458

Appendix C Installing and Configuring the AWS CLI 461

Mac OS Users 461

Installing the AWS CLI 461

Configuring the AWS CLI 462

Windows Users 464

Installing the AWS CLI4 64

Configuring the AWS CLI 465

Appendix D Introduction to NumPy and Pandas 467

NumPy 467

Creating NumPy Arrays 467

Modifying Arrays 471

Indexing and Slicing 474

Pandas 475

Creating Series and Dataframes 476

Getting Dataframe Information 478

Selecting Data 481

Index 485

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