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Applied Machine Learning and High-Performance Computing on AWS

You're reading from   Applied Machine Learning and High-Performance Computing on AWS Accelerate the development of machine learning applications following architectural best practices

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803237015
Length 382 pages
Edition 1st Edition
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Authors (4):
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Trenton Potgieter Trenton Potgieter
Author Profile Icon Trenton Potgieter
Trenton Potgieter
Shreyas Subramanian Shreyas Subramanian
Author Profile Icon Shreyas Subramanian
Shreyas Subramanian
Farooq Sabir Farooq Sabir
Author Profile Icon Farooq Sabir
Farooq Sabir
Mani Khanuja Mani Khanuja
Author Profile Icon Mani Khanuja
Mani Khanuja
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Toc

Table of Contents (20) Chapters Close

Preface 1. Part 1: Introducing High-Performance Computing
2. Chapter 1: High-Performance Computing Fundamentals FREE CHAPTER 3. Chapter 2: Data Management and Transfer 4. Chapter 3: Compute and Networking 5. Chapter 4: Data Storage 6. Part 2: Applied Modeling
7. Chapter 5: Data Analysis 8. Chapter 6: Distributed Training of Machine Learning Models 9. Chapter 7: Deploying Machine Learning Models at Scale 10. Chapter 8: Optimizing and Managing Machine Learning Models for Edge Deployment 11. Chapter 9: Performance Optimization for Real-Time Inference 12. Chapter 10: Data Visualization 13. Part 3: Driving Innovation Across Industries
14. Chapter 11: Computational Fluid Dynamics 15. Chapter 12: Genomics 16. Chapter 13: Autonomous Vehicles 17. Chapter 14: Numerical Optimization 18. Index 19. Other Books You May Enjoy

The high availability of model endpoints

Amazon SageMaker provides fault tolerance and high availability of the deployed endpoints. In this section, we will discuss various features and options of AWS cloud infrastructure and Amazon SageMaker, that we can use to ensure that our endpoints are fault-tolerant, resilient, and highly available.

Deployment on multiple instances

SageMaker gives us the option of deploying our endpoints on multiple instances. This protects from instance failures. If one instance goes down, then other instances can still serve the inference requests. In addition, if our endpoints are deployed on multiple instances and an availability zone outage occurs or an instance fails, SageMaker automatically tries to distribute our instances across different availability zones, thereby improving the resiliency of our endpoints. It is also a good practice to deploy our endpoints using small instance types spread across different availability zones.

Endpoints autoscaling...

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