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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

Summary

In this chapter, we started with understanding the concepts of genomics and how you can store and manage large genomics data on AWS. We also discussed the end-to-end architecture design for transferring, storing, analyzing, and applying ML to genomics data using AWS services. We then focused on how you can deploy large state-of-the-art models for genomics, such as DNABERT, for promoter recognition tasks using Amazon SageMaker with a few lines of code and how you can test your endpoint using code and the SageMaker Studio UI.

We then moved on to understanding proteomics, which is the study of protein sequences, structure, and their functions. We walked through an example of predicting protein secondary structure for protein sequences using a Hugging Face pretrained model with 11 billion parameters. Since it is a large model with memory requirements greater than 220 GB, we explored various memory-saving techniques, such as activation checkpointing, activation offloading, optimizer...

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