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Java Concurrency and Parallelism

Java Concurrency and Parallelism

By : Jay Wang
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Java Concurrency and Parallelism

Java Concurrency and Parallelism

5 (2)
By: Jay Wang

Overview of this book

If you’re a software developer, architect, or systems engineer, exploring Java’s concurrency utilities and synchronization in the cloud, this book is an essential resource. Tech visionary Jay Wang, with over three decades of experience transforming industry giants, brings unparalleled expertise to guide you through Java’s concurrency and parallel processing in cloud computing. This comprehensive book starts by establishing the foundational concepts of concurrency and parallelism, vital for cloud-native development, and gives you a complete overview, highlighting challenges and best practices. Wang expertly demonstrates Java’s role in big data, machine learning, microservices, and serverless computing, shedding light on how Java’s tools are effectively utilized in these domains. Complete with practical examples and insights, this book bridges theory with real-world applications, ensuring a holistic understanding of Java in cloud-based scenarios. You’ll navigate advanced topics, such as synchronizing Java’s concurrency with cloud auto-scaling and GPU computing, and be equipped with the skills and foresight to tackle upcoming trends in cloud technology. This book serves as your roadmap to innovation and excellence in Java cloud applications, giving you in-depth knowledge and hands-on practice for mastering Java in the cloud era.
Table of Contents (20 chapters)
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Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing
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Part 2: Java's Concurrency in Specialized Domains
12
Part 3: Mastering Concurrency in the Cloud – The Final Frontier

Achieving scalable ML deployments using Java’s concurrency APIs

Before delving into the specific strategies for leveraging Java’s concurrency APIs in ML deployments, it’s essential to understand the critical role these APIs play in the modern ML landscape. ML tasks often require processing vast amounts of data and performing complex computations that can be highly time-consuming. Java’s concurrency APIs enable the execution of multiple parts of these tasks in parallel, significantly speeding up the process and improving the efficiency of resource utilization. This capability is indispensable for scaling ML deployments, allowing them to handle larger datasets and more sophisticated models without compromising performance.

To achieve scalable ML deployments using Java’s concurrency APIs, we can consider the following strategies and techniques:

  • Data preprocessing: Leverage parallelism to preprocess large datasets efficiently. Utilize Java...
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