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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

Chapter 5: Building and Training ML Models with SageMaker Studio IDE

Building and training a machine learning (ML) model can be easy with SageMaker Studio. It is an integrated development environment (IDE) designed for ML developers for building and training ML models at scale and efficiently. In order to train an ML model, you may previously have dealt with the cumbersome overhead of managing compute infrastructure for yourself or for your team to train ML models properly. You may also have experienced compute resource constraints, either on desktop machines or with cloud resources, where you are given a fixed-size instance. When you develop in SageMaker Studio, there is no more frustration with provisioning and managing compute infrastructure because you can easily make use of elastic compute in SageMaker Studio and its wide support of sophisticated ML algorithms and frameworks for your ML use case.

In this chapter, we will be covering the following topics:

  • Training models...
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