You're reading from Machine Learning Engineering on AWS Build, scale, and secure machine learning systems and MLOps pipelines in production

Product type Paperback

Published in Oct 2022

Publisher Packt

ISBN-13 9781803247595

Length 530 pages

Edition 1st Edition

Tools

AWS

Concepts

Machine Learning

Author (1):

Joshua Arvin Lat

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Table of Contents (19) Chapters

Preface

1. Part 1: Getting Started with Machine Learning Engineering on AWS

2. Chapter 1: Introduction to ML Engineering on AWS FREE CHAPTER

3. Chapter 2: Deep Learning AMIs

4. Chapter 3: Deep Learning Containers

5. Part 2:Solving Data Engineering and Analysis Requirements

6. Chapter 4: Serverless Data Management on AWS

7. Chapter 5: Pragmatic Data Processing and Analysis

8. Part 3: Diving Deeper with Relevant Model Training and Deployment Solutions

9. Chapter 6: SageMaker Training and Debugging Solutions

10. Chapter 7: SageMaker Deployment Solutions

11. Part 4:Securing, Monitoring, and Managing Machine Learning Systems and Environments

12. Chapter 8: Model Monitoring and Management Solutions

13. Chapter 9: Security, Governance, and Compliance Strategies

14. Part 5:Designing and Building End-to-end MLOps Pipelines

15. Chapter 10: Machine Learning Pipelines with Kubeflow on Amazon EKS

16. Chapter 11: Machine Learning Pipelines with SageMaker Pipelines

17. Index

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Figure 4.35 – How Amazon Athena works

If you were to set this up yourself, you may need to set up an EC2 instance cluster with an application such as Presto. In addition to this, you will need to manage the overall cost, security, performance, and stability of this EC2 cluster setup yourself.

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If the Before you run your first query, you need to set up a query result location in Amazon S3 notification appears on the Editor page, this means that you must make a quick configuration change on the Amazon Athena Settings page so that Athena can store the query results in a specified S3 bucket location every time there’...

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You're reading from Machine Learning Engineering on AWS Build, scale, and secure machine learning systems and MLOps pipelines in production

Table of Contents (19) Chapters

Using Amazon Athena to query data in Amazon S3

Setting up the query result location

Authors (1)

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You're reading from Machine Learning Engineering on AWS Build, scale, and secure machine learning systems and MLOps pipelines in production

Table of Contents (19) Chapters

Using Amazon Athena to query data in Amazon S3

Setting up the query result location

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Authors (1)

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