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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

Managing ML workflows with SageMaker Pipelines

SageMaker Pipelines is a purpose-built CI/CD and orchestration service that helps automate, manage, and reuse machine learning workflows. It has tight integration with the different features and capabilities of SageMaker, which makes it easy for data scientists and machine learning engineers to use it for MLOps requirements with the SageMaker service.

In this recipe, we will use SageMaker Pipelines to create and manage automated ML workflows. We will work with a simplified example involving a sequential workflow of a processing step, followed by a training step. The processing step makes use of SageMaker Processing to perform the train-test split, while the training step focuses on training a linear learner model using the training data that's been prepared by the processing step. Once we have completed the steps in this recipe, we will be able to execute an end-to-end automated pipeline using SageMaker Pipelines, without having...

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