Time Series Analysis on AWS
Packt Publishing Limited (Verlag)
978-1-80181-684-7 (ISBN)
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 DescriptionBeing 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 forIf 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.
Michael 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.
Table of Contents
An Overview of Time Series Analysis
An Overview of Amazon Forecast
Creating a Project and Ingesting Your Data
Training a Predictor with AutoML
Customizing Your Predictor Training
Generating New Forecasts
Improving and Scaling Your Forecast Strategy
An Overview of Amazon Lookout for Equipment
Creating a Dataset and Ingesting Your Data
Training and Evaluating a Model
Scheduling Regular Inferences
Reducing Time to Insights for Anomaly Detections
An Overview of Amazon Lookout for Metrics
Creating and Activating a Detector
Viewing Anomalies and Providing Feedback
Erscheinungsdatum | 05.03.2022 |
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Verlagsort | Birmingham |
Sprache | englisch |
Maße | 75 x 93 mm |
Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
Informatik ► Software Entwicklung ► SOA / Web Services | |
Mathematik / Informatik ► Informatik ► Web / Internet | |
ISBN-10 | 1-80181-684-0 / 1801816840 |
ISBN-13 | 978-1-80181-684-7 / 9781801816847 |
Zustand | Neuware |
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