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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

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

View More author details

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

Why subscribe?

18. Other Books You May Enjoy

Further reading

For more information on the topics covered in this chapter, feel free to check out the following resources:

Kubernetes concepts (https://kubernetes.io/docs/concepts/)
Getting started with Amazon EKS (https://docs.aws.amazon.com/eks/latest/userguide/getting-started.html)
eksctl – The official CLI for Amazon EKS (https://eksctl.io/)
Amazon EKS troubleshooting (https://docs.aws.amazon.com/eks/latest/userguide/troubleshooting.html)
Kubeflow on AWS – Deployment (https://awslabs.github.io/kubeflow-manifests/docs/deployment/)
Kubeflow on AWS Security (https://awslabs.github.io/kubeflow-manifests/docs/about/security/)

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Authors (1)

Joshua Arvin Lat

Joshua Arvin Lat

Joshua Arvin Lat is the Chief Technology Officer (CTO) of NuWorks Interactive Labs, Inc. He previously served as the CTO for three Australian-owned companies and as director of software development and engineering for multiple e-commerce start-ups in the past. Years ago, he and his team won first place in a global cybersecurity competition with their published research paper. He is also an AWS Machine Learning Hero and has shared his knowledge at several international conferences, discussing practical strategies on machine learning, engineering, security, and management.

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