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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Automating data preparation and analysis with AWS Glue DataBrew

AWS Glue DataBrew is a no-code data preparation service built to help data scientists and ML engineers clean, prepare, and transform data. Similar to the services we used in Chapter 4, Serverless Data Management on AWS, Glue DataBrew is serverless as well. This means that we won’t need to worry about infrastructure management when using this service to perform data preparation, transformation, and analysis.

Figure 5.2 – The core concepts in AWS Glue DataBrew

In Figure 5.2, we can see that there are different concepts and resources involved when using AWS Glue DataBrew. We need to have a good idea of what these are before using the service. Here is a quick overview of the concepts and terms used:

Dataset – Data stored in an existing data source (for example, Amazon S3, Amazon Redshift, or Amazon RDS) or uploaded from the local machine to an S3 bucket.
Recipe –...

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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

Automating data preparation and analysis with AWS Glue DataBrew

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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

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