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Machine Learning Engineering on AWS

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

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803247595
Length 530 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 (19) Chapters Close

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 18. Other Books You May Enjoy

Serverless Data Management on AWS

Businesses generally utilize systems that collect and store user information, along with transaction data, inside databases. One good example of this would be an e-commerce startup that has a web application where customers can create an account and use their credit card to make online purchases. The user profiles, transaction data, and purchase history stored in several production databases can be used to build a product recommendation engine, which can help suggest products that customers would probably want to purchase as well. However, before this stored data is analyzed and used to train machine learning (ML) models, it must be merged and joined into a centralized data store so that it can be transformed and processed using a variety of tools and services. Several options are frequently used for these types of use cases, but we will focus on two of these in this chapter – data warehouses and data lakes.

Data warehouses and data lakes...

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