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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

Designing an architecture for AV systems

In this section, we will be discussing a reference architecture published by AWS called the Autonomous Driving Data Lake Reference Architecture, a link to which can be found in the References section.

The complete architecture is replicated in Figure 13.3:

Figure 13.3 – Autonomous Driving Data Lake Reference Architecture

Figure 13.3 – Autonomous Driving Data Lake Reference Architecture

In this section, we will zoom into parts of this architecture to discuss it in further detail. Let’s start with data ingestion:

  • Figure 13.4 shows how cars may be installed with a data logger or some removable storage media that stores data from sensors. Custom hardware or AWS Outposts can be used to process data that is stored from one or more trips. For near real time, AWS IoT core can be used along with Amazon Kinesis Firehose to deliver data to Amazon S3. Customers can also use Amazon Direct Connect, as mentioned earlier in this chapter, for secure and fast data transfer...
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