You're reading from Java Concurrency and Parallelism Master advanced Java techniques for cloud-based applications through concurrency and parallelism

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781805129264

Length 496 pages

Edition 1st Edition

Languages

Java

Tools

Boost

Concepts

Application Development

Author (1):

Jay Wang

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Table of Contents (20) Chapters

Preface

1. Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing

2. Chapter 1: Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape FREE CHAPTER

3. Chapter 2: Introduction to Java’s Concurrency Foundations: Threads, Processes, and Beyond

4. Chapter 3: Mastering Parallelism in Java

5. Chapter 4: Java Concurrency Utilities and Testing in the Cloud Era

6. Chapter 5: Mastering Concurrency Patterns in Cloud Computing

7. Part 2: Java's Concurrency in Specialized Domains

8. Chapter 6: Java and Big Data – a Collaborative Odyssey

9. Chapter 7: Concurrency in Java for Machine Learning

10. Chapter 8: Microservices in the Cloud and Java’s Concurrency

11. Chapter 9: Serverless Computing and Java’s Concurrent Capabilities

12. Part 3: Mastering Concurrency in the Cloud – The Final Frontier

13. Chapter 10: Synchronizing Java’s Concurrency with Cloud Auto-Scaling Dynamics

14. Chapter 11: Advanced Java Concurrency Practices in Cloud Computing

15. Chapter 12: The Horizon Ahead

16. Index

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Appendix A: Setting up a Cloud-Native Java Environment

1. Appendix B: Resources and Further Reading

AI and ML integration

As we look toward the future of Java in cloud computing, the integration of AI and ML presents exciting opportunities and challenges. While Chapter 7 focused on Java’s concurrency mechanisms for ML workflows, this section explores Java’s evolving role in cloud-based AI/ML ecosystems and its integration with advanced cloud AI services.

Java’s position in cloud-based AI/ML workflows

Here are some of Java’s evolving roles in cloud-based AI/ML ecosystems:

Serverless AI/ML with Java: The future of Java in cloud-based AI/ML workflows is increasingly serverless. Frameworks such as AWS Lambda and Google Cloud Functions allow developers to deploy AI/ML models as serverless functions. This trend is expected to grow, enabling more efficient and scalable AI/ML operations without the need for managing infrastructure.
Java as an orchestrator: Java is positioning itself as a powerful orchestrator for complex AI/ML pipelines in the...