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Modern Data Architecture on AWS

You're reading from   Modern Data Architecture on AWS A Practical Guide for Building Next-Gen Data Platforms on AWS

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781801813396
Length 420 pages
Edition 1st Edition
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Author (1):
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Behram Irani Behram Irani
Author Profile Icon Behram Irani
Behram Irani
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Table of Contents (24) Chapters Close

Preface 1. Part 1: Foundational Data Lake
2. Prologue: The Data and Analytics Journey So Far FREE CHAPTER 3. Chapter 1: Modern Data Architecture on AWS 4. Chapter 2: Scalable Data Lakes 5. Part 2: Purpose-Built Services And Unified Data Access
6. Chapter 3: Batch Data Ingestion 7. Chapter 4: Streaming Data Ingestion 8. Chapter 5: Data Processing 9. Chapter 6: Interactive Analytics 10. Chapter 7: Data Warehousing 11. Chapter 8: Data Sharing 12. Chapter 9: Data Federation 13. Chapter 10: Predictive Analytics 14. Chapter 11: Generative AI 15. Chapter 12: Operational Analytics 16. Chapter 13: Business Intelligence 17. Part 3: Govern, Scale, Optimize And Operationalize
18. Chapter 14: Data Governance 19. Chapter 15: Data Mesh 20. Chapter 16: Performant and Cost-Effective Data Platform 21. Chapter 17: Automate, Operationalize, and Monetize 22. Index 23. Other Books You May Enjoy

AWS AI/ML services overview

AWS provides a very broad set of AI/ML services, right from specialized infrastructure and ML frameworks that allow ML engineers to custom train their ML models and deploy them on custom hardware. This includes ML frameworks such as PyTorch, TensorFlow, and Apache MXNet. ML infrastructure often requires plenty of CPU and GPU power. AWS provides many types of Amazon Elastic Compute Cloud (Amazon EC2) instances such as the P3 and Trn1 instances that are suitable for ML training. AWS also provides ML accelerators such as AWS Trainium for DL training and AWS Inferentia for high-performance ML inferences.

The next layer of services revolves around ML. AWS ML services are created specifically keeping in mind many of the barriers to ML adoption. In order to democratize ML, it is essential to have different services geared toward different personas in the organization. For the same reason, AWS has created ML services that can help multiple personas, even those...

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