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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

Invoking machine learning models with Amazon Athena using SQL queries

Amazon Athena is a serverless interactive query service that helps us analyze data in Amazon S3 using SQL syntax. As it is a serverless service, machine learning practitioners no longer need to manage any infrastructure, so we can focus on the work that needs to be done. If you have used or heard of Amazon Athena before, you must be aware that this solution can easily scale and support big data requirements. Amazon Athena also supports a variety of data formats (such as CSV and text files), columnar formats (such as Parquet and ORC), and compressed data formats (such as Snappy and GZIP).

Note

Of course, this is a simplified description of what serverless is all about. Feel free to check https://aws.amazon.com/serverless/ for more information.

In this recipe, we will use Amazon Athena to analyze our dataset stored in Amazon S3 using SQL statements. We will make use of a deployed machine learning model within...

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