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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

Chapter 1: Getting Started with Machine Learning Using Amazon SageMaker

Machine learning (ML) is one of the most important topics in the world right now. Through the use of different algorithms and models, it can solve different practical problems and requirements, such as anomaly detection, forecasting, spam detection, image classification, and more. Performing a few experiments in your local machine will help get things started. However, once we need to deal with end-to-end experiments involving larger datasets, deep learning requirements, and production-grade model deployments, we will need a more dedicated set of solutions to help us effectively manage these experiments.

This is what Amazon SageMaker aims to accomplish. Amazon SageMaker is a fully managed ML service that brings together different solutions to speed up the process of preparing, building, training, and deploying ML models. As we go through each of the chapters in this book, we will see how it helps get things...

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