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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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Author (1):
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Neylson Crepalde Neylson Crepalde
Author Profile Icon Neylson Crepalde
Neylson Crepalde
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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

Generative AI on Kubernetes

Generative artificial intelligence (GenAI) has emerged as a transformative technology, revolutionizing how we interact with and leverage AI. In this chapter, we will explore the exciting world of generative AI and learn how to harness its power on Kubernetes. We will dive into the fundamentals of generative AI and understand its main differences from traditional AI.

Our focus will be on leveraging Amazon Bedrock, a comprehensive suite of services designed to simplify the development and deployment of generative AI applications. Through hands-on examples, you will gain practical experience in building a generative AI application on Kubernetes using Streamlit, a powerful Python library for creating interactive data applications. We will cover the entire process, from the development to deploying the application on a Kubernetes cluster.

Moreover, we will explore the concept of retrieval-augmented generation (RAG), which combines the power of generative...

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