Book Image

Implementing Cloud Design Patterns for AWS - Second Edition

By : Sean Keery, Clive Harber, Marcus Young
Book Image

Implementing Cloud Design Patterns for AWS - Second Edition

By: Sean Keery, Clive Harber, Marcus Young

Overview of this book

Whether you're just getting your feet wet in cloud infrastructure or already creating complex systems, this book will guide you through using the patterns to fit your system needs. Starting with patterns that cover basic processes such as source control and infrastructure-as-code, the book goes on to introduce cloud security practices. You'll then cover patterns of availability and scalability and get acquainted with the ephemeral nature of cloud environments. You'll also explore advanced DevOps patterns in operations and maintenance, before focusing on virtualization patterns such as containerization and serverless computing. In the final leg of your journey, this book will delve into data persistence and visualization patterns. You'll get to grips with architectures for processing static and dynamic data, as well as practices for managing streaming data. By the end of this book, you will be able to design applications that are tolerant of underlying hardware failures, resilient against an unexpected influx of data, and easy to manage and replicate.
Table of Contents (20 chapters)
Title Page
Dedication
About Packt
Contributors
Preface
Free Chapter
1
Introduction to Amazon Web Services
Index

Machine learning


Being able to anticipate your customers' needs, or find patterns across your data, are growth areas for businesses right now—this type of business intelligence is currently the holy grail. Being able to design and train Artificial Intelligence (AI) to find patterns and connections in your data lake gives the users of your application a better experience.

This section covers the various tools and concepts for machine learning and AI available for AWS customers.

In particular, you will discover these two tools:

  • Amazon SageMaker
  • Amazon Comprehend

And we will cover the concepts of the following:

  • AI
  • Anomaly detection
  • Prediction
  • Forecasting

Amazon SageMaker

SageMaker can be used to build, train, and deploy machine learning models quickly. As a fully managed service, it covers the entire workflow: labeling and preparing data; choosing and training an algorithm; tuning and optimizing for deployment; making predictions; and taking actions.

 

 

To set up SageMaker, you first need to create a model...