You're reading from Learn Amazon SageMaker A guide to building, training, and deploying machine learning models for developers and data scientists

Product type Paperback

Published in Aug 2020

Publisher Packt

ISBN-13 9781800208919

Length 490 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Machine Learning

Author (1):

Julien Simon

Preface

1. Section 1: Introduction to Amazon SageMaker

2. Chapter 1: Introduction to Amazon SageMaker FREE CHAPTER

3. Chapter 2: Handling Data Preparation Techniques

4. Section 2: Building and Training Models

5. Chapter 3: AutoML with Amazon SageMaker Autopilot

6. Chapter 4: Training Machine Learning Models

7. Chapter 5: Training Computer Vision Models

8. Chapter 6: Training Natural Language Processing Models

9. Chapter 7: Extending Machine Learning Services Using Built-In Frameworks

10. Chapter 8: Using Your Algorithms and Code

11. Section 3: Diving Deeper on Training

12. Chapter 9: Scaling Your Training Jobs

13. Chapter 10: Advanced Training Techniques

14. Section 4: Managing Models in Production

15. Chapter 11: Deploying Machine Learning Models

16. Chapter 12: Automating Machine Learning Workflows

17. Chapter 13: Optimizing Prediction Cost and Performance

18. Other Books You May Enjoy

Autoscaling an endpoint

Autoscaling has long been the most important technique in adjusting infrastructure size to incoming traffic, and it's available for SageMaker endpoints. However, it's based on Application Autoscaling, and not on EC2 Autoscaling (https://docs.aws.amazon.com/autoscaling/application/userguide/what-is-application-auto-scaling.html), although the concepts are extremely similar.

Let's set up autoscaling for the XGBoost model we trained on the Boston Housing dataset:

We first create an Endpoint Configuration, and we use it to build the endpoint. Here, we use the m5 instance family: t2 and t3 are not recommended for autoscaling as their burstable behavior makes it harder to measure their real load:
```
model_name = 'sagemaker-xgboost-2020-06-09-08-33-24-782'endpoint_config_name = 'xgboost-one-model-epc'endpoint_name = 'xgboost-one-model-ep'
production_variants = [{    'VariantName': &apos...
```