Cover
Acknowledgments
About the Author
About the Technical Editor
Introduction
1. Who This Book Is For
2. What This Book Covers
3. How This Book Is Structured
4. What You Need to Use This Book
5. Conventions
6. Source Code
7. Errata
Part 1: Fundamentals of Machine Learning
1. Chapter 1: Introduction to Machine Learning
  1. What Is Machine Learning?
  2. Types of Machine Learning Systems
  3. The Traditional Versus the Machine Learning Approach
  4. Summary
2. Chapter 2: Data Collection and Preprocessing
  1. Machine Learning Datasets
  2. Data Preprocessing Techniques
  3. Summary
3. Chapter 3: Data Visualization with Python
  1. Introducing Matplotlib
  2. Components of a Plot
  3. Common Plots
  4. Summary
4. Chapter 4: Creating Machine Learning Models with Scikit-learn
  1. Introducing Scikit-learn
  2. Creating a Training and Test Dataset
  3. Creating Machine Learning Models
  4. Summary
5. Chapter 5: Evaluating Machine Learning Models
  1. Evaluating Regression Models
  2. Evaluating Classification Models
  3. Choosing Hyperparameter Values
  4. Summary
Part 2: Machine Learning with Amazon Web Services
1. Chapter 6: Introduction to Amazon Web Services
  1. What Is Cloud Computing?
  2. Cloud Service Models
  3. Cloud Deployment Models
  4. The AWS Ecosystem
  5. Sign Up for an AWS Free-Tier Account
  6. Summary
  7. Note
2. Chapter 7: AWS Global Infrastructure
  1. Regions and Availability Zones
  2. Edge Locations
  3. Accessing AWS
  4. Summary
3. Chapter 8: Identity and Access Management
  1. Key Concepts
  2. Common Tasks
  3. Summary
4. Chapter 9: Amazon S3
  1. Key Concepts
  2. Common Tasks
  3. Summary
5. Chapter 10: Amazon Cognito
  1. Key Concepts
  2. Common Tasks
  3. User Pools or Identity Pools: Which One Should You Use?
  4. Summary
6. Chapter 11: Amazon DynamoDB
  1. Key Concepts
  2. Common Tasks
  3. Summary
7. Chapter 12: AWS Lambda
  1. Common Use Cases for Lambda
  2. Key Concepts
  3. Common Tasks
  4. Summary
8. Chapter 13: Amazon Comprehend
  1. Key Concepts
  2. Text Analysis Using the Amazon Comprehend Management Console
  3. Interactive Text Analysis with the AWS CLI
  4. Using Amazon Comprehend with AWS Lambda
  5. Summary
9. Chapter 14: Amazon Lex
  1. Key Concepts
  2. Creating an Amazon Lex Bot
  3. Summary
10. Chapter 15: Amazon Machine Learning
  1. Key Concepts
  2. Creating Datasources
  3. Viewing Data Insights
  4. Creating an ML Model
  5. Making Batch Predictions
  6. Creating a Real-Time Prediction Endpoint for Your Machine Learning Model
  7. Making Predictions Using the AWS CLI
  8. Using Real-Time Prediction Endpoints with Your Applications
  9. Summary
11. Chapter 16: Amazon SageMaker
  1. Key Concepts
  2. Creating an Amazon SageMaker Notebook Instance
  3. Preparing Test and Training Data
  4. Training a Scikit-Learn Model on an Amazon SageMaker Notebook Instance
  5. Training a Scikit-Learn Model on a Dedicated Training Instance
  6. Training a Model Using a Built-in Algorithm on a Dedicated Training Instance
  7. Summary
12. Chapter 17: Using Google TensorFlow with Amazon SageMaker
  1. Introduction to Google TensorFlow
  2. Creating a Linear Regression Model with Google TensorFlow
  3. Training and Deploying a DNN Classifier Using the TensorFlow Estimators API and Amazon SageMaker
  4. Summary
13. Chapter 18: Amazon Rekognition
  1. Key Concepts
  2. Analyzing Images Using the Amazon Rekognition Management Console
  3. Interactive Image Analysis with the AWS CLI
  4. Using Amazon Rekognition with AWS Lambda
  5. Summary
14. Appendix A: Anaconda and Jupyter Notebook Setup
  1. Installing the Anaconda Distribution
  2. Creating a Conda Python Environment
  3. Installing Python Packages
  4. Installing Jupyter Notebook
  5. Summary
15. Appendix B: AWS Resources Needed to Use This Book
  1. Creating an IAM User for Development
  2. Creating S3 Buckets
16. Appendix C: Installing and Configuring the AWS CLI
  1. Mac OS Users
  2. Windows Users
17. Appendix D: Introduction to NumPy and Pandas
  1. NumPy
  2. Pandas
Index
End User License Agreement

List of Tables

Chapter 1
1. TABLE 1.1: Type and Range of Data across 100 Sample Applications
2. TABLE 1.2: Transforming Categorical Features into Numeric Features
3. TABLE 1.3: Modified Input Features
Chapter 7
1. TABLE 7.1: AWS Regions and Availability Zones
Chapter 9
1. TABLE 9.1: Amazon S3 System-Defined Metadata
Chapter 12
1. TABLE 12.1: Common Event Sources for AWS Lambda
Chapter 14
1. TABLE 14.1: ACMEBankAccount Table Items
2. TABLE 14.2: ACMEAccountTransaction Table Items
3. TABLE 14.3: ViewTransactionList Intent Slots
Chapter 15
1. TABLE 15.1: The First Five Rows of the Titanic Dataset
2. TABLE 15.2: The First Ten Rows of the Batch Prediction Result
Chapter 18
1. TABLE 18.1: Aggregate Metric Graphs
Appendix C
1. TABLE C.1: AWS Region Names
2. TABLE C.2 AWS Region Names
Appendix D
1. TABLE D.1: Commonly Used Ndarray Attributes

List of Illustrations

Chapter 1
1. FIGURE 1.1 Supervised learning
2. FIGURE 1.2 Clustering technique used to find patterns in the data
3. FIGURE 1.3 Semi-supervised learning
4. FIGURE 1.4 Architecture of a rule-based decision system
5. FIGURE 1.5 A flowchart depicting the decision-making process for a rule-base...
6. FIGURE 1.6 Cross-validation using multiple folds
7. FIGURE 1.7 The sigmoid function
8. FIGURE 1.8 Using the sigmoid function for binary classification
Chapter 2
1. FIGURE 2.1 The head() function displays rows from the beginning of a Pandas ...
2. FIGURE 2.2 The head() function displays truncated data for large dataframes.
3. FIGURE 2.3 Impact of the set_index function on a dataframe
4. FIGURE 2.4 Distribution of values for the Survived attribute
5. FIGURE 2.5 Histogram of numeric features
6. FIGURE 2.6 Histogram of numeric feature “Age” using different bin widths (2,...
7. FIGURE 2.7 Histogram of categorical feature “Embarked”
8. FIGURE 2.8 Box plot of numeric features
9. FIGURE 2.9 Linear correlation between numeric columns
10. FIGURE 2.10 Matrix of scatter plots between pairs of numeric attributes
11. FIGURE 2.11 Box plot of the Age feature variable
12. FIGURE 2.12 Dataframe with engineered feature AgeCategory
13. FIGURE 2.13 Dataframe with engineered feature FareCategory
14. FIGURE 2.14 Histogram of Age, NormalizedAge, and StandardizedAge
Chapter 3
1. FIGURE 3.1 Plotting two curves using Matplotlib
2. FIGURE 3.2 Components of a Matplotlib plot
3. FIGURE 3.3 A figure object with four axes objects
4. FIGURE 3.4 Comparison of plots with and without grids
5. FIGURE 3.5 Histogram of Passenger Age values
6. FIGURE 3.6 Histograms of Passenger Age values created using different binnin...
7. FIGURE 3.7 Bar chart of theEmbarked attribute
8. FIGURE 3.8 Grouped bar chart of the Embarked attribute
9. FIGURE 3.9 Stacked bar chart of the Embarked attribute
10. FIGURE 3.10 Stacked percentage bar chart of the Embarked attribute
11. FIGURE 3.11 Pie chart of proportion of passengers embarking from different p...
12. FIGURE 3.12 Pie charts showing the proportion of survivors from each embarka...
13. FIGURE 3.13 Box plot showing the distribution of the Age attribute
14. FIGURE 3.14 Box plots of the Age attribute comparing the distribution of sur...
15. FIGURE 3.15 Scatter plot of the Age attribute against the Fare attribute
16. FIGURE 3.16 Scatter plots depicting the ideal strong positive and strong neg...
17. FIGURE 3.17 Scatter plot matrix of the features of the Iris dataset
18. FIGURE 3.18 Scatter plot of four clusters of data
Chapter 4
1. FIGURE 4.1 Scikit-learn's train_test_split() method automatically shuffles t...
2. FIGURE 4.2 Comparison of the distribution of target variables in the origina...
3. FIGURE 4.3 Comparison of the distribution of target variables in the origina...
4. FIGURE 4.4 Cross-validation using k-folds
5. FIGURE 4.5 Scatter plot of expected vs. predicted house prices
6. FIGURE 4.6 Scatter plot of synthetic dataset along with regression lines
7. FIGURE 4.7 Three potential decision boundaries
8. FIGURE 4.8 Data that cannot be classified using a linear decision boundary i...
9. FIGURE 4.9 Data that cannot be classified using a linear decision boundary i...
10. FIGURE 4.10 Nonlinear decision boundary in two-dimensional space
11. FIGURE 4.11 Effect of kernel choice on decision boundaries
12. FIGURE 4.12 Linear regression vs. support vector regression
13. FIGURE 4.13 SVR predictions on Boston housing dataset
14. FIGURE 4.14 The sigmoid function
15. FIGURE 4.15 Using the sigmoid function for binary classification
16. FIGURE 4.16 Softmax logistic regression
17. FIGURE 4.17 Decision tree visualization
18. FIGURE 4.18 Decision tree for regression
Chapter 5
1. FIGURE 5.1 Comparison of predictive accuracies of a linear regression model ...
2. FIGURE 5.2 Mean squared error and root mean squared error
3. FIGURE 5.3 A class-wise confusion matrix
4. FIGURE 5.4 ROC curves for three binary classification models
5. FIGURE 5.5 Multi-class confusion matrix for a five-class dataset
6. FIGURE 5.6 Multi-class confusion matrix for two models trained on the Iris f...
Chapter 6
1. FIGURE 6.1 Common cloud service models
2. FIGURE 6.2 Brief timeline of Amazon Web Services
3. FIGURE 6.3 Amazon Web Services home page
4. FIGURE 6.4 AWS sign-in screen
5. FIGURE 6.5 Contact Information screen
6. FIGURE 6.6 Payment Information screen
7. FIGURE 6.7 Phone Verification screen
8. FIGURE 6.8 Phone verification PIN
9. FIGURE 6.9 Completing the identity verification process
10. FIGURE 6.10 Support plan selection
11. FIGURE 6.11 Completing the sign-up process
Chapter 7
1. FIGURE 7.1 Multiple Availability Zones in a single region
2. FIGURE 7.2 Geographically distant users accessing a video file from Tokyo
3. FIGURE 7.3 Edge locations can be used to cache frequently used content
4. FIGURE 7.4 AWS home page
5. FIGURE 7.5 AWS management console home page
6. FIGURE 7.6 AWS management console menu bar
7. FIGURE 7.7 Accessing the Services menu in the AWS management console
8. FIGURE 7.8 Resource Groups menu
9. FIGURE 7.9 Creating a resource group
10. FIGURE 7.10 Tagged resources are visible in the Resource Groups menu.
11. FIGURE 7.11 Resources in the CustomerAPI-Infrastructure resource group
12. FIGURE 7.12 Account menu
13. FIGURE 7.13 Regions menu
Chapter 8
1. FIGURE 8.1 IAM users exist under the root AWS account.
2. FIGURE 8.2 Obtaining temporary credentials
3. FIGURE 8.3 IAM groups contain users and permissions.
4. FIGURE 8.4 Root account login screen
5. FIGURE 8.5 IAM user-specific login screen
6. FIGURE 8.6 AWS management console region selector
7. FIGURE 8.7 Accessing the IAM management console
8. FIGURE 8.8 User-specific IAM sign-in link
9. FIGURE 8.9 IAM resource dashboard
10. FIGURE 8.10 Creating an IAM user
11. FIGURE 8.11 User details screen
12. FIGURE 8.12 Configuring user permissions
13. FIGURE 8.13 Creating a new group
14. FIGURE 8.14 The new group appears alongside existing groups.
15. FIGURE 8.15 The EC2FullAccess policy loaded in the policy editor
16. FIGURE 8.16 Review user settings screen
17. FIGURE 8.17 User confirmation screen
18. FIGURE 8.18 List of groups
19. FIGURE 8.19 Group permissions summary
20. FIGURE 8.20 Creating a new role using the IAM console
21. FIGURE 8.21 Creating a service role for EC2 instances
22. FIGURE 8.22 Attaching a policy to a role
23. FIGURE 8.23 You can associate up to 50 optional tags with a role.
24. FIGURE 8.24 Review new role screen
25. FIGURE 8.25 Accessing MFA settings
26. FIGURE 8.26 Configure security credentials warning
27. FIGURE 8.27 The Activate MFA button is enabled.
28. FIGURE 8.28 Choosing the MFA device type
29. FIGURE 8.29 Configuring a step-up authenticator
30. FIGURE 8.30 IAM password policy settings
Chapter 9
1. FIGURE 9.1 Accessing the Amazon S3 management console
2. FIGURE 9.2 Amazon S3 management console welcome page
3. FIGURE 9.3 List of Amazon S3 buckets
4. FIGURE 9.4 Specifying the bucket name and region
5. FIGURE 9.5 Configuring versioning, logging, and cost allocation tags
6. FIGURE 9.6 Configuring bucket permissions
7. FIGURE 9.7 Bucket summary page
8. FIGURE 9.8 List of Amazon S3 buckets in your account
9. FIGURE 9.9 Contents of an Amazon S3 bucket
10. FIGURE 9.10 Selecting files in the File Upload dialog box
11. FIGURE 9.11 Configuring object permissions
12. FIGURE 9.12 Configuring file storage class and encryption
13. FIGURE 9.13 File summary page
14. FIGURE 9.14 Amazon S3 bucket showing a file
15. FIGURE 9.15 Downloading a file from a bucket
16. FIGURE 9.16 Locating the Amazon S3 Object URL
17. FIGURE 9.17 Non-public buckets and files are not accessible using a URL.
18. FIGURE 9.18 Accessing Amazon S3 bucket permissions
19. FIGURE 9.19 Configuring Amazon S3 bucket permissions
20. FIGURE 9.20 Accessing the Make Public option
21. FIGURE 9.21 Making a file publicly accessible
22. FIGURE 9.22 Changing the storage class of an object
23. FIGURE 9.23 Deleting an object from an Amazon S3 bucket
24. FIGURE 9.24 Enabling bucket versioning
25. FIGURE 9.25 Making an object publicly accessible while uploading it
26. FIGURE 9.26 Accessing document versions
27. FIGURE 9.27 Version selector switch
Chapter 10
1. FIGURE 10.1 Accessing the S3 management console
2. FIGURE 10.2 Amazon Cognito splash screen
3. FIGURE 10.3 Creating a new user pool
4. FIGURE 10.4 Specifying the name of the new user pool
5. FIGURE 10.5 User pool attributes
6. FIGURE 10.6 Adding a custom attribute to a user pool
7. FIGURE 10.7 Setting up user pool policies
8. FIGURE 10.8 Multifactor authentication settings for the user pool
9. FIGURE 10.9 Customizing email and SMS verification messages
10. FIGURE 10.10 Cost allocation tag setup screen
11. FIGURE 10.11 You can set up a user pool to remember devices
12. FIGURE 10.12 Configuring applications that can use the user pool to authenti...
13. FIGURE 10.13 Create Application screen
14. FIGURE 10.14 List of client applications in the user pool
15. FIGURE 10.15 Use triggers to call AWS Lambda functions at specific points in...
16. FIGURE 10.16 User pool Review screen
17. FIGURE 10.17 Click the Show Details button to reveal the app client ID and t...
18. FIGURE 10.18 Amazon Cognito splash screen
19. FIGURE 10.19 Creating a new identity pool
20. FIGURE 10.20 List of existing identity pools
21. FIGURE 10.21 Specifying the Amazon Cognito user pool ID and app client ID
22. FIGURE 10.22 Cognito, by default, creates new roles for authenticated and un...
23. FIGURE 10.23 Accessing the credentials needed to access AWS services
Chapter 11
1. FIGURE 11.1 Accessing the Amazon DynamoDB service home page
2. FIGURE 11.2 Amazon DynamoDB splash screen
3. FIGURE 11.3 Amazon DynamoDB dashboard
4. FIGURE 11.4 Specifying a table name
5. FIGURE 11.5 Specifying a composite key for a table
6. FIGURE 11.6 Changing the provisioned I/O capacity
7. FIGURE 11.7 Amazon DynamoDB table overview
8. FIGURE 11.8 Creating a new item in the customer table
9. FIGURE 11.9 Item attributes dialog showing default primary key attribute
10. FIGURE 11.10 Adding item attributes
11. FIGURE 11.11 Specifying multiple attributes
12. FIGURE 11.12 Viewing item attributes as JSON
13. FIGURE 11.13 Amazon DynamoDB table with one item
14. FIGURE 11.14 Each item in an Amazon DynamoDB table can have different attrib...
15. FIGURE 11.15 Creating an index
16. FIGURE 11.16 Index properties dialog
17. FIGURE 11.17 Amazon DynamoDB table index list
18. FIGURE 11.18 Mandatory fields for new items
19. FIGURE 11.19 Multiple items in an Amazon DynamoDB table
20. FIGURE 11.20 List of items returned as a result of a scan operation
21. FIGURE 11.21 Adding a filter expression to a scan
22. FIGURE 11.22 Indexes can be used while performing a scan.
23. FIGURE 11.23 Switching from Scan mode to Query mode
24. FIGURE 11.24 Querying a DynamoDB table based on the partition key
Chapter 12
1. FIGURE 12.1 AWS Lambda service home page
2. FIGURE 12.2 AWS Lambda splash screen
3. FIGURE 12.3 AWS Lambda dashboard
4. FIGURE 12.4 List of existing AWS Lambda functions
5. FIGURE 12.5 AWS Lambda Create Function screen
6. FIGURE 12.6 Lambda function Name and Runtime settings
7. FIGURE 12.7 Inspecting the permissions policy document associated with the I...
8. FIGURE 12.8 Lambda function configuration page
9. FIGURE 12.9 List of AWS Lambda functions
10. FIGURE 12.10 Updating the code for the AWS Lambda function
11. FIGURE 12.11 List of AWS Lambda functions
12. FIGURE 12.12 Configuring a test event
13. FIGURE 12.13 Configuring a test event
14. FIGURE 12.14 AWS Lambda function execution results
15. FIGURE 12.15 Accessing AWS Lambda function execution statistics and logs
16. FIGURE 12.16 Accessing the Delete function menu item
17. FIGURE 12.17 Accessing the Amazon CloudWatch dashboard
18. FIGURE 12.18 List of Amazon CloudWatch log groups
19. FIGURE 12.19 Accessing the Delete Log Group menu item
Chapter 13
1. FIGURE 13.1 Accessing the Amazon Comprehend service home page
2. FIGURE 13.2 Testing the capabilities of Amazon Comprehend
3. FIGURE 13.3 Analyzing text with Amazon Comprehend
4. FIGURE 13.4 Amazon Comprehend presents analysis results as insights.
5. FIGURE 13.5 AWS Lambda splash screen
6. FIGURE 13.6 AWS Lambda dashboard
7. FIGURE 13.7 Creating an AWS Lambda function from scratch
8. FIGURE 13.8 Lambda Function Name and Runtime settings
9. FIGURE 13.9 Viewing the default policy document associated with the IAM role...
10. FIGURE 13.10 Updating the default policy document associated with the IAM ro...
11. FIGURE 13.11 Review Policy screen
12. FIGURE 13.12 AWS Lambda function designer
13. FIGURE 13.13 Adding the Amazon S3 trigger to the AWS Lambda function
14. FIGURE 13.14 Configuring the Amazon S3 event trigger
15. FIGURE 13.15 Accessing the function code editor
Chapter 14
1. FIGURE 14.1 Accessing the Amazon DynamoDB service home page
2. FIGURE 14.2 Amazon DynamoDB splash screen
3. FIGURE 14.3 Amazon DynamoDB dashboard
4. FIGURE 14.4 Specifying the table name, partition key, and sort key
5. FIGURE 14.5 Changing the provisioned I/O capacity
6. FIGURE 14.6 Amazon DynamoDB table overview
7. FIGURE 14.7 Settings for the ACMEAccountTransaction table
8. FIGURE 14.8 Amazon DynamoDB table overview
9. FIGURE 14.9 Creating a new item in the ACMEBankCustomer table
10. FIGURE 14.10 ACMEBankCustomer table with two items
11. FIGURE 14.11 ACMEBankAccount table with five items
12. FIGURE 14.12 Creating an AWS Lambda function from scratch
13. FIGURE 14.13 Lambda function name and runtime settings
14. FIGURE 14.14 Viewing the default policy document associated with the IAM rol...
15. FIGURE 14.15 Updating the default policy document associated with the IAM ro...
16. FIGURE 14.16 Review Policy screen
17. FIGURE 14.17 AWS Lambda function designer
18. FIGURE 14.18 Accessing the Amazon Lex service home page
19. FIGURE 14.19 Amazon Lex service splash screen
20. FIGURE 14.20 Amazon Lex dashboard
21. FIGURE 14.21 Creating a custom bot
22. FIGURE 14.22 Amazon Lex bot editor
23. FIGURE 14.23 Configuring the slots for your new intent.
24. FIGURE 14.24 Specifying the name of the new intent
25. FIGURE 14.25 Amazon Lex bot editor with two intents
26. FIGURE 14.26 The Sample Utterances section of the bot editor
27. FIGURE 14.27 Utterances associated with the AccountOverview intent
28. FIGURE 14.28 Specifying the validation function for the AccountOverview inte...
29. FIGURE 14.29 Slots for the AccountOverview intent
30. FIGURE 14.30 CustomerIdentifier slot settings
31. FIGURE 14.31 Specifying the Fulfillment function for the AccountOverview int...
32. FIGURE 14.32 Specifying the validation function for the ViewTransactionList ...
33. FIGURE 14.33 Specifying the fulfillment function for the ViewTransactionList...
34. FIGURE 14.34 Building the bot
35. FIGURE 14.35 Testing the bot with the integrated chat client
Chapter 15
1. FIGURE 15.1 Uploading the Titanic dataset to an Amazon S3 bucket
2. FIGURE 15.2 Accessing the Amazon Machine Learning service home page
3. FIGURE 15.3 The Amazon Machine Learning service home page
4. FIGURE 15.4 Accessing the Amazon Machine Learning dashboard
5. FIGURE 15.5 Accessing the Create Datasource option from the Amazon Machine L...
6. FIGURE 15.6 Specifying the location of the input file
7. FIGURE 15.7 Granting Amazon Machine Learning access to your Amazon S3 bucket
8. FIGURE 15.8 Modifying the default schema generated by Amazon Machine Learnin...
9. FIGURE 15.9 Specifying the target attribute
10. FIGURE 15.10 Specifying a row identifier attribute
11. FIGURE 15.11 Datasource Review screen
12. FIGURE 15.12 Filtering the items displayed in the Amazon Machine Learning da...
13. FIGURE 15.13 Specifying the location of the data for the new datasource
14. FIGURE 15.14 Setting up the schema for the new datasource
15. FIGURE 15.15 The new datasource does not have a target attribute.
16. FIGURE 15.16 Specifying a row identifier attribute
17. FIGURE 15.17 Selecting the datasource from the dashboard
18. FIGURE 15.18 Histogram of the target attribute
19. FIGURE 15.19 Summary statistics for categorical values
20. FIGURE 15.20 Distribution of values of the Embarked attribute
21. FIGURE 15.21 Rows that do not have a value for the Embarked attribute
22. FIGURE 15.22 Distribution of the Cabin attribute
23. FIGURE 15.23 Summary statistics for numeric attributes
24. FIGURE 15.24 Distribution of values for the Age attribute
25. FIGURE 15.25 Creating an ML model
26. FIGURE 15.26 Selecting a datasource
27. FIGURE 15.27 Specifying ML model settings
28. FIGURE 15.28 Amazon Machine Learning dashboard showing new data sources, the...
29. FIGURE 15.29 ML model summary
30. FIGURE 15.30 ML model evaluation
31. FIGURE 15.31 Advanced ML model statistics
32. FIGURE 15.32 A score of 0.37 results in a model accuracy of 0.8507 (85.07%).
33. FIGURE 15.33 Accessing the option to create a new batch prediction from the ...
34. FIGURE 15.34 Selecting an ML model for batch predictions
35. FIGURE 15.35 Selecting a datasource for batch predictions
36. FIGURE 15.36 Specifying an Amazon S3 bucket where the results of the batch p...
37. FIGURE 15.37 Batch Prediction Review screen
38. FIGURE 15.38 Amazon Machine Learning dashboard showing a completed batch pre...
39. FIGURE 15.39 Amazon S3 Bucket with the results of the batch prediction
40. FIGURE 15.40 Creating a real-time prediction endpoint for an Amazon Machine ...
41. FIGURE 15.41 Costs of maintaining a real-time prediction endpoint
42. FIGURE 15.42 Accessing the real-time prediction endpoint
Chapter 16
1. FIGURE 16.1 Enabling region-specific Amazon STS endpoints
2. FIGURE 16.2 Accessing the Amazon SageMaker management console
3. FIGURE 16.3 Navigating to the list of notebook instances
4. FIGURE 16.4 Specifying the name of the new Amazon SageMaker notebook instanc...
5. FIGURE 16.5 Creating a new IAM role for the Amazon SageMaker notebook instan...
6. FIGURE 16.6 Specifying the permissions policy for the new IAM role for Amazo...
7. FIGURE 16.7 New IAM role for Amazon SageMaker
8. FIGURE 16.8 Amazon SageMaker management console showing the new notebook ins...
9. FIGURE 16.9 Amazon SageMaker notebook instance management
10. FIGURE 16.10 Accessing the Amazon S3 bucket that will contain the training a...
11. FIGURE 16.11 Uploading the pre-split training and test data files to the Ama...
12. FIGURE 16.12 Creating a new Jupyter Notebook on an Amazon SageMaker notebook...
13. FIGURE 16.13 Changing the title of a Jupyter Notebook file
14. FIGURE 16.14 Uploading a file to a notebook instance
15. FIGURE 16.15 Using a notebook instance to create a training job
16. FIGURE 16.16 List of trained models
17. FIGURE 16.17 Training a model based on a built-in algorithm using an AWS Sag...
Chapter 17
1. FIGURE 17.1 Structure of an artificial neural network (ANN)
2. FIGURE 17.2 A simple neural network
3. FIGURE 17.3 TensorFlow API architecture
4. FIGURE 17.4 Accessing the Amazon S3 bucket that will contain the training an...
5. FIGURE 17.5 Uploading the pre-split training and test data files to the Amaz...
6. FIGURE 17.6 Amazon SageMaker management console showing the new notebook ins...
7. FIGURE 17.7 Inspecting the first five rows of the Boston housing dataset
8. FIGURE 17.8 Mean squared error metric
9. FIGURE 17.9 Computation graph with two placeholder nodes
10. FIGURE 17.10 Computation graph with two variable nodes
11. FIGURE 17.11 Computation graph after the multiplication of w1 and x1 nodes
12. FIGURE 17.12 Computation graph with y_predicted
13. FIGURE 17.13 Computation graph that contains nodes to compute the MSE cost f...
14. FIGURE 17.14 Computation graph that contains the operation to optimize the c...
15. FIGURE 17.15 Uploading a file to a notebook instance
16. FIGURE 17.16 Architecture of neural-network–based classification model
17. FIGURE 17.17 Using a notebook instance to create a training job
18. FIGURE 17.18 List of trained models
Chapter 18
1. FIGURE 18.1 Accessing the Amazon Rekognition service home page
2. FIGURE 18.2 Accessing the Object and Scene Detection demo
3. FIGURE 18.3 Object labels detected in a sample scene
4. FIGURE 18.4 Amazon Rekognition aggregate metric graphs
5. FIGURE 18.5 Accessing the Amazon DynamoDB management console
6. FIGURE 18.6 Amazon DynamoDB table name and primary key attributes
7. FIGURE 18.7 Amazon DynamoDB Table Read/Write Capacity Mode section
8. FIGURE 18.8 Amazon DynamoDB management console displaying a list of tables
9. FIGURE 18.9 Creating an AWS Lambda function from scratch
10. FIGURE 18.10 Lambda Function Name and Runtime settings
11. FIGURE 18.11 Viewing the default policy document associated with the IAM rol...
12. FIGURE 18.12 Updating the default policy document associated with the IAM ro...
13. FIGURE 18.13 Review Policy screen
14. FIGURE 18.14 AWS Lambda function designer
15. FIGURE 18.15 Configuring the S3 event trigger
16. FIGURE 18.16 Configuring the AWS Lambda function code
17. FIGURE 18.17 Examining the results of the AWS Lambda function
18. FIGURE 18.18 Querying the Amazon DynamoDB table will allow you to search for...

Introduction

Amazon Web Services (AWS) is one of the leading cloud-computing platforms in the industry today. At the time this book was written, AWS offered more than 100 services, each of which resided in one of 18 different service categories. For someone who is new to cloud computing or to the AWS ecosystem, the sheer number of services on offer can be daunting. It can be difficult to know where to begin and what services to focus on.

Developers who are new to machine learning as well as experienced data scientists are often not aware of the power of the public cloud and AWS's offerings in the machine learning space in particular. In the past, cloud-based machine learning offerings have been limited in the types of algorithms they could support and the level of customization that was possible. All of this changed when Amazon announced SageMaker—a service that provided the ability to build machine learning models based on Amazon's implementation of cutting-edge algorithms, as well as the option to build custom models with frameworks such as Scikit-learn and Google TensorFlow.

Real-world use cases of cloud-based machine learning models are not based on using the model in isolation, but instead rely on a number of supporting systems such as databases, load balancers, API gateways, and identity providers, all of which are provided by AWS. This book is written to provide both seasoned machine learning experts and enthusiasts alike an introduction to a selection of AWS machine learning services that are based on pre-trained models, as well as step-by-step examples of how to train and deploy your own custom models on Amazon SageMaker. For enthusiasts who are new to machine learning, this book also provides a selection of chapters that cover the fundamentals of machine learning such as data preprocessing, visualization, feature engineering, and the use of common Python libraries such as NumPy, Pandas, and Scikit-learn.

This book at all times attempts to balance between theory and practice, giving you enough visibility into the underlying concepts and providing you with the best practices and practical advice that you can apply at your workplace right away. I have also made every attempt to keep the content up-to-date and relevant. Even though this makes the book susceptible to being outdated in a few rare instances, I am confident the content will remain useful and relevant through the next versions of the AWS services.

Who This Book Is For

This book is best suited for software developers who wish to learn about machine learning in general and how to leverage machine learning–specific offerings from AWS. The book is also useful to data scientists, system architects, and application architects, who want to get an introduction to some of the commonly used AWS services in the machine learning space.

If you are new to both machine learning and AWS, I advise that you read all chapters from start to finish. If you are an experienced data scientist, you may want to skip ahead to Part 2 to learn about machine learning–specific AWS services.

What This Book Covers

This book covers building and training machine learning models with Python on the AWS cloud, as well as a number of ready-to-use machine learning services such as Amazon Rekognition, Amazon Comprehend, and Amazon Lex.

The book also covers general high-level concepts of machine learning, including feature engineering, data visualization, as well as supporting AWS services that are used to build machine learning systems such as Amazon IAM, Amazon Cognito, Amazon S3, Amazon DynamoDB, and AWS Lambda.

The model-building and evaluation code in this book is written in Python 3. Services provided by Amazon, Apple, and Google are updated frequently and therefore sometimes you may encounter a newer version of a screen when you follow the instructions in a chapter.

How This Book Is Structured

This book consists of 18 chapters that are grouped into two parts, and four appendices. The first part consists of five chapters and covers the fundamentals of machine learning using Python. This part covers techniques for feature engineering, data visualization, model building, and model evaluation using Pandas, NumPy, Matplotlib, and Scikit-learn. The examples developed in this part make use of Jupyter Notebook and are aimed at readers who are new to machine learning.

Part 2 covers building machine learning applications using AWS services. This part starts with introducing the basics of commonly used AWS services such as Amazon S3, Amazon DynamoDB, and AWS Lambda. It then proceeds to AWS services that deal specifically with machine learning such as Amazon Comprehend, Amazon Lex, Amazon Machine Learning, and Amazon SageMaker. Two chapters are dedicated to Amazon SageMaker; the first one covers building and deploying models using built-in algorithms and Scikit-learn, and the second one covers building and deploying a model with Google TensorFlow. Not all chapters in this part include source code, but where applicable, you can download the source code that accompanies each chapter using a GitHub link. Some of the chapters in this part require you to upload files to Amazon S3; you will need to substitute the names of buckets in the examples with those from your own account.

The chapters in Part 1 include:

Introduction to Machine Learning (Chapter 1) This is an introduction to the types of machine learning systems, their applications, and tools used to build machine learning systems.
Data Collection and Preprocessing (Chapter 2) This chapter covers sources that can be used to obtain training data, techniques to explore datasets, and basic feature engineering.
Data Visualization with Python (Chapter 3) This chapter covers techniques to visualize datasets using Matplotlib.
Creating Machine Learning Models with Scikit-learn (Chapter 4) This chapter covers techniques to build and train classification and regression models using Scikit-learn.
Evaluating Machine Learning Models (Chapter 5) This chapter covers techniques to evaluate the quality of a machine learning model.

The chapters in Part 2 include:

Introduction to Amazon Web Services (Chapter 6) This chapter is a brief primer on cloud computing and Amazon Web Services. It also covers commonly encountered service and deployment models.
AWS Global Infrastructure (Chapter 7) This chapter introduces AWS regions, availability zones, and edge locations.
Identity and Access Management (Chapter 8) This chapter introduces one of the key services provided by AWS to secure your resources in the Amazon cloud. It also provides instructions to sign up for an account under the AWS free tier.
Amazon S3 (Chapter 9) This chapter introduces one the most commonly used storage services provided by AWS, Amazon Simple Storage Service (S3).
Amazon Cognito (Chapter 10) This chapter introduces Amazon's cloud-based OAuth2.0-compliant identity management solution, Amazon Cognito.
Amazon DynamoDB (Chapter 11) This chapter introduces Amazon's managed NoSQL database service, Amazon DynamoDB.
AWS Lambda (Chapter 12) This chapter introduces AWS Lambda, a service designed to allow you to run code in the Amazon cloud without having to provision or manage any infrastructure.
Amazon Comprehend (Chapter 13) This chapter introduces Amazon Comprehend, a cloud-based natural language processing service that you can integrate into your applications to analyze the contents of text documents.
Amazon Lex (Chapter 14) This chapter introduces Amazon Lex, a cloud-based service that you can use to create chatbots and integrate them into your applications.
Amazon Machine Learning (Chapter 15) This chapter introduces Amazon Machine Learning, a fully managed cloud-based service that you can use to build and deploy simple machine learning models without any programming.
Amazon SageMaker (Chapter 16) This chapter introduces Amazon SageMaker, a cloud-based machine learning service that can be used to train and deploy both built-in and custom machine learning models.
Using Google Tensorflow with Amazon SageMaker (Chapter 17) This chapter introduces Google's Tensorflow framework and covers the use of Amazon SageMaker to build and deploy Tensorflow models.
Amazon Rekognition (Chapter 18) This chapter introduces Amazon Rekognition, a fully managed cloud-based service that can be used to add computer vision capabilities to your applications.

The appendices cover the following topics:

Anaconda and Jupyter Notebook Setup (Appendix A) This appendix provides instructions to install the Anaconda distribution and set up a Jupyter Notebook server on your local computer.
AWS Resources Needed to Use This Book (Appendix B) This appendix provides information on the AWS resources that you need to set up in your account in order to follow along with the examples in the book.
Installing and Configuring the AWS CLI (Appendix C) This appendix provides instructions to download and install the AWS CLI tool.
Introduction to NumPy and Pandas (Appendix D) This appendix provides an introduction to two Python libraries commonly used by data scientists: NumPy and Pandas.

What You Need to Use This Book

A suitable Mac or Windows computer for development
Basic knowledge of Python programming
An AWS account that you can administer

Conventions

To help you get the most from the text and keep track of what's happening, we've used a number of conventions throughout the book.

NOTE Notes, tips, hints, tricks, and asides to the current discussion are offset like this.

As for styles in the text:

We italicize new terms and important words when we introduce them.
We show keyboard strokes like this: Ctrl+A.
We show filenames, URLs, and code within the text like so: persistence.properties.
We present code in two different ways:
We use a monofont type with no highlighting for most code examples.

We use bold type to emphasize code that is of particular importance in the present context.

Source Code

As you work through the examples in this book, you may choose either to type in all the code manually or to use the source code files that accompany the book. All of the source code used in this book is available for download at www.wiley.com/go/machinelearningawscloud. Also, you can download the code files at GitHub.

Errata

We make every effort to ensure that there are no errors in the text or in the code. However, no one is perfect, and mistakes do occur. If you find an error in one of our books, like a spelling mistake or faulty piece of code, we would be very grateful for your feedback. By sending in errata you may save another reader hours of frustration and at the same time you will be helping us provide even higher quality information.

To report errata, email to errata@wiley.com and include

The book's title and ISBN (Machine Learning in the AWS Cloud, 9781119556718)
The page number of the relevant content
A description of just what's wrong

Machine Learning in the AWS Cloud

Add Intelligence to Applications with Amazon SageMaker and Amazon Rekognition

Acknowledgments

About the Author

About the Technical Editor