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Serverless Machine Learning with Amazon Redshift ML

You're reading from  Serverless Machine Learning with Amazon Redshift ML

Product type Book
Published in Aug 2023
Publisher Packt
ISBN-13 9781804619285
Pages 290 pages
Edition 1st Edition
Languages
Authors (4):
Debu Panda Debu Panda
Profile icon Debu Panda
Phil Bates Phil Bates
Profile icon Phil Bates
Bhanu Pittampally Bhanu Pittampally
Profile icon Bhanu Pittampally
Sumeet Joshi Sumeet Joshi
Profile icon Sumeet Joshi
View More author details

Table of Contents (19) Chapters

Preface 1. Part 1:Redshift Overview: Getting Started with Redshift Serverless and an Introduction to Machine Learning
2. Chapter 1: Introduction to Amazon Redshift Serverless 3. Chapter 2: Data Loading and Analytics on Redshift Serverless 4. Chapter 3: Applying Machine Learning in Your Data Warehouse 5. Part 2:Getting Started with Redshift ML
6. Chapter 4: Leveraging Amazon Redshift ML 7. Chapter 5: Building Your First Machine Learning Model 8. Chapter 6: Building Classification Models 9. Chapter 7: Building Regression Models 10. Chapter 8: Building Unsupervised Models with K-Means Clustering 11. Part 3:Deploying Models with Redshift ML
12. Chapter 9: Deep Learning with Redshift ML 13. Chapter 10: Creating a Custom ML Model with XGBoost 14. Chapter 11: Bringing Your Own Models for Database Inference 15. Chapter 12: Time-Series Forecasting in Your Data Warehouse 16. Chapter 13: Operationalizing and Optimizing Amazon Redshift ML Models 17. Index 18. Other Books You May Enjoy

What is Amazon Forecast?

Amazon Forecast, like Amazon Redshift ML, requires no ML experience to use. Time-series forecasts are generated using various ML and statistical algorithms based on historical data. As a user, you simply send data to Amazon Forecast and it will examine the data and automatically identify what is meaningful and produces a forecasting model.

With Amazon Redshift ML, you can leverage Amazon Forecast to create and train forecasting models from your time-series data and use these models to generate forecasts. For forecasting, we require a target time-series dataset. In target time-series forecasting, we predict the future value of a variable using the past data or previous values, which is often called univariate time series because the data is sequential over equal time increments. Currently, Redshift ML supports target time-series datasets with a custom domain. The dataset in your data warehouse must contain the frequency or interval at which you capture your...

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