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Machine Learning with Amazon SageMaker Cookbook

You're reading from   Machine Learning with Amazon SageMaker Cookbook 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781800567030
Length 762 pages
Edition 1st Edition
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Author (1):
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Joshua Arvin Lat Joshua Arvin Lat
Author Profile Icon Joshua Arvin Lat
Joshua Arvin Lat
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Table of Contents (11) Chapters Close

Preface 1. Chapter 1: Getting Started with Machine Learning Using Amazon SageMaker 2. Chapter 2: Building and Using Your Own Algorithm Container Image FREE CHAPTER 3. Chapter 3: Using Machine Learning and Deep Learning Frameworks with Amazon SageMaker 4. Chapter 4: Preparing, Processing, and Analyzing the Data 5. Chapter 5: Effectively Managing Machine Learning Experiments 6. Chapter 6: Automated Machine Learning in Amazon SageMaker 7. Chapter 7: Working with SageMaker Feature Store, SageMaker Clarify, and SageMaker Model Monitor 8. Chapter 8: Solving NLP, Image Classification, and Time-Series Forecasting Problems with Built-in Algorithms 9. Chapter 9: Managing Machine Learning Workflows and Deployments 10. Other Books You May Enjoy

Launching an Amazon SageMaker Notebook Instance

In this recipe, we will set up an Amazon SageMaker notebook instance where we can run our ML experiments using Jupyter Notebooks. The SageMaker notebook instance is a fully managed ML compute instance running a collection of tools and applications such as the Jupyter Notebook app. With several tools and libraries already installed and ready to use, we can go straight into working on our ML experiments without having to worry about the installation and maintenance work.

Important note

Take note that we can also perform our ML experiments in Amazon SageMaker Studio. We will take a closer look at Amazon SageMaker Studio in Chapter 6, Automated Machine Learning in Amazon SageMaker. From our end, it is critical to know how to use both of them as there will be features and capabilities such as local mode, which is supported in notebook instances, but not supported in Amazon SageMaker Studio.

Getting ready

Here are the prerequisites...

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