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.

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