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Big Data on Kubernetes

You're reading from   Big Data on Kubernetes A practical guide to building efficient and scalable data solutions

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835462140
Length 296 pages
Edition 1st Edition
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Concepts
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Author (1):
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Neylson Crepalde Neylson Crepalde
Author Profile Icon Neylson Crepalde
Neylson Crepalde
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Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1:Docker and Kubernetes FREE CHAPTER
2. Chapter 1: Getting Started with Containers 3. Chapter 2: Kubernetes Architecture 4. Chapter 3: Getting Hands-On with Kubernetes 5. Part 2: Big Data Stack
6. Chapter 4: The Modern Data Stack 7. Chapter 5: Big Data Processing with Apache Spark 8. Chapter 6: Building Pipelines with Apache Airflow 9. Chapter 7: Apache Kafka for Real-Time Events and Data Ingestion 10. Part 3: Connecting It All Together
11. Chapter 8: Deploying the Big Data Stack on Kubernetes 12. Chapter 9: Data Consumption Layer 13. Chapter 10: Building a Big Data Pipeline on Kubernetes 14. Chapter 11: Generative AI on Kubernetes 15. Chapter 12: Where to Go from Here 16. Index 17. Other Books You May Enjoy

Summary

In this chapter, we explored the exciting world of generative AI and learned how to harness its power on Kubernetes. We started by understanding the fundamental concepts of generative AI, its underlying mechanisms, and how it differs from traditional machine learning approaches.

We then leveraged Amazon Bedrock, a comprehensive suite of services, to build and deploy generative AI applications. We learned how to work with Bedrock’s foundational models, such as Claude 3 Haiku and Claude 3 Sonnet, and how to integrate them into a Streamlit application for interactive user experiences.

Next, we delved into the concept of RAG, which combines the power of generative AI with external knowledge bases. We built a RAG system using Knowledge Bases for Amazon Bedrock, enabling our application to access and leverage vast amounts of structured data, improving the accuracy and relevance of the generated output.

Finally, we explored Agents for Amazon Bedrock, a powerful feature...

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