Leverage AWS AI/ML managed services to generate value from your time series data

Key Features

• Solve modern time series analysis problems such as forecasting and anomaly detection
• Gain a solid understanding of AWS AI/ML managed services and apply them to your business problems
• Explore different algorithms to build applications that leverage time series data

Book Description

Being a business analyst and data scientist, you have to use many algorithms and approaches to prepare, process, and build ML-based applications by leveraging time series data, but you face common problems, such as not knowing which algorithm to choose or how to combine and interpret them. Amazon Web Services (AWS) provides numerous services to help you build applications fueled by artificial intelligence (AI) capabilities. This book helps you get to grips with three AWS AI/ML-managed services to enable you to deliver your desired business outcomes.

The book begins with Amazon Forecast, where you'll discover how to use time series forecasting, leveraging sophisticated statistical and machine learning algorithms to deliver business outcomes accurately. You'll then learn to use Amazon Lookout for Equipment to build multivariate time series anomaly detection models geared toward industrial equipment and understand how it provides valuable insights to reinforce teams focused on predictive maintenance and predictive quality use cases. In the last chapters, you'll explore Amazon Lookout for Metrics, and automatically detect and diagnose outliers in your business and operational data.

By the end of this AWS book, you'll have understood how to use the three AWS AI services effectively to perform time series analysis.

What you will learn

• Understand how time series data differs from other types of data
• Explore the key challenges that can be solved using time series data
• Forecast future values of business metrics using Amazon Forecast
• Detect anomalies and deliver forewarnings using Lookout for Equipment
• Detect anomalies in business metrics using Amazon Lookout for Metrics
• Visualize your predictions to reduce the time to extract insights

Who this book is for

If you're a data analyst, business analyst, or data scientist looking to analyze time series data effectively for solving business problems, this is the book for you. Basic statistics knowledge is assumed, but no machine learning knowledge is necessary. Prior experience with time series data and how it relates to various business problems will help you get the most out of this book. This guide will also help machine learning practitioners find new ways to leverage their skills to build effective time series-based applications.

Table of Contents

1. An Overview of Time Series Analysis
2. An Overview of Amazon Forecast
3. Creating a Project and Ingesting Your Data
4. Training a Predictor with AutoML
5. Customizing Your Predictor Training
6. Generating New Forecasts
7. Improving and Scaling Your Forecast Strategy
8. An Overview of Amazon Lookout for Equipment
9. Creating a Dataset and Ingesting Your Data
10. Training and Evaluating a Model
11. Scheduling Regular Inferences
12. Reducing Time to Insights for Anomaly Detections
13. An Overview of Amazon Lookout for Metrics
14. Creating and Activating a Detector
15. Viewing Anomalies and Providing Feedback

Review

"Michaël Hoarau introduces us to common characteristics and challenges specific to time-series data analysis, while inspiring us with examples of the insights and opportunities that may be hidden in our data. It’s an excellent handbook for identifying best practice approaches, covering examples showing when and how to use common open source packages and when to use powerful new AWS managed services to dramatically simplify time-series analysis using AI/ML. Hands-on readers will love the companion GitHub repository with all the code used in the exercises and examples throughout the book."

-- Bob Strahan, Principal Solutions Architect, Amazon Web Services (AWS)

About the Author

Michaël Hoarau is an AI/ML specialist solutions architect (SA) working at Amazon Web Services (AWS). He is an AWS Certified Associate SA. He previously worked as an AI/ML specialist SA at AWS and the EMEA head of data science at GE Digital. He has experience in building product quality prediction systems for multiple industries. He has used forecasting techniques to build virtual sensors for industrial production lines. He has also helped multiple customers build forecasting and anomaly detection systems to increase their business efficiency.