Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Serverless Machine Learning with Amazon Redshift ML

You're reading from   Serverless Machine Learning with Amazon Redshift ML Create, train, and deploy machine learning models using familiar SQL commands

Arrow left icon
Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781804619285
Length 290 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (4):
Arrow left icon
Phil Bates Phil Bates
Author Profile Icon Phil Bates
Phil Bates
Sumeet Joshi Sumeet Joshi
Author Profile Icon Sumeet Joshi
Sumeet Joshi
Debu Panda Debu Panda
Author Profile Icon Debu Panda
Debu Panda
Bhanu Pittampally Bhanu Pittampally
Author Profile Icon Bhanu Pittampally
Bhanu Pittampally
Arrow right icon
View More author details
Toc

Table of Contents (19) Chapters Close

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 FREE CHAPTER 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

Operationalizing your ML models

Once a model is validated and used on a regular basis for running predictions, it should be operationalized. The reasons for this are to remove the manual tasks of retraining your models and to ensure that your model still retains high accuracy after your data distribution has changed over time, also referred to as data drift. When data drift occurs, you need to retrain the model using an updated training set.

In the following sections, we will do a simple model retraining, then show you how you can create a version from an existing model.

Model retraining process without versioning

To walk through the retraining process, we will use one of our previously used models.

In Chapter 7, we discussed different regression models, so let’s use the chapter7_regressionmodel.predict_ticket_price_auto model. This model solved a multi-input regression problem and SageMaker Autopilot chose the XGBoost algorithm.

Let’s assume this model...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at R$50/month. Cancel anytime