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Getting Started with Amazon SageMaker Studio

You're reading from   Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781801070157
Length 326 pages
Edition 1st Edition
Languages
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Author (1):
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Michael Hsieh Michael Hsieh
Author Profile Icon Michael Hsieh
Michael Hsieh
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Table of Contents (16) Chapters Close

Preface 1. Part 1 – Introduction to Machine Learning on Amazon SageMaker Studio
2. Chapter 1: Machine Learning and Its Life Cycle in the Cloud FREE CHAPTER 3. Chapter 2: Introducing Amazon SageMaker Studio 4. Part 2 – End-to-End Machine Learning Life Cycle with SageMaker Studio
5. Chapter 3: Data Preparation with SageMaker Data Wrangler 6. Chapter 4: Building a Feature Repository with SageMaker Feature Store 7. Chapter 5: Building and Training ML Models with SageMaker Studio IDE 8. Chapter 6: Detecting ML Bias and Explaining Models with SageMaker Clarify 9. Chapter 7: Hosting ML Models in the Cloud: Best Practices 10. Chapter 8: Jumpstarting ML with SageMaker JumpStart and Autopilot 11. Part 3 – The Production and Operation of Machine Learning with SageMaker Studio
12. Chapter 9: Training ML Models at Scale in SageMaker Studio 13. Chapter 10: Monitoring ML Models in Production with SageMaker Model Monitor 14. Chapter 11: Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry 15. Other Books You May Enjoy

Optimizing your model deployment

Optimizing model deployment is a critical topic for businesses. No one wants to be spending a dime more than they need to. Because deployed endpoints are being used continuously, and incurring charges continuously, making sure that the deployment is optimized in terms of cost and runtime performance can save you a lot of money. SageMaker has several options to help you reduce costs while optimizing the runtime performance. In this section, we will be discussing multi-model endpoint deployment and how to choose the instance type and autoscaling policy for your use case.

Hosting multi-model endpoints to save costs

A multi-model endpoint is a type of real-time endpoint in SageMaker that allows multiple models to be deployed behind the same endpoint. There are many use cases in which you would build models for each customer or for each geographic area, and depending on the characteristics of the incoming data point, you would apply the corresponding...

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