Machine Learning in the AWS Cloud

Book description

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

  1. Cover
  2. Acknowledgments
  3. About the Author
  4. About the Technical Editor
  5. 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
  6. 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
  7. 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
  8. Index
  9. End User License Agreement

Product information

  • Title: Machine Learning in the AWS Cloud
  • Author(s): Abhishek Mishra
  • Release date: September 2019
  • Publisher(s): Sybex
  • ISBN: 9781119556718