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7.9 MB
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5B71ED97D34A2B499E71FAB6AE9A8D801C863928
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July 4, 2025, 9:15 a.m.
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(Last updated: July 4, 2025, 9:16 a.m.)
| File | Size |
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| ['Pandiya D. AI and Microservices. Integrating AI into API Design...2025.pdf'] | 0 bytes |
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23.7 MB
[13
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1]
2023-07-01
| Uploaded by indexFroggy | Size 23.7 MB | Health [ 13 /1 ] | Added 2023-07-01 |
NOTE
SOURCE: Pandiya D. AI and Microservices. Integrating AI into API Design...2025
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COVER
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MEDIAINFO
Textbook in PDF format This book explores how Artificial Intelligence (AI) is transforming the design and operation of microservices and API architecture. It provides a clear and practical guide to using AI to automate tasks, enhance performance, and improve the scalability of microservice-based systems. Starting with the basics, you will learn about the core concepts of microservices and API design, gradually building an understanding of how AI can be seamlessly integrated. Through real-world examples, visual diagrams, and mock APIs, the book shows you how to bring theory into practice, making complex systems easier to manage and more efficient. You will also discover strategies for testing and scaling systems, securing APIs, and addressing ethical challenges in AI-powered environments. Case studies highlight successful implementations, offering valuable insights you can apply to your own projects. Whether you're a developer, architect, or tech enthusiast, this book gives you the tools and inspiration to build smarter, more resilient systems while staying ahead of future trends in AI and distributed computing. AI in software development integrates machine learning, automation, and information evaluation to enhance the development lifecycle. These features can improve proficiency, adaptability, and reliability as well as reduce costs and enhance the client experience. Machine Learning (ML), which is part of AI, integrates a revolutionary shift in software development. It empowers systems to learn capabilities from data and optimize itself to perform other functionalities than inherent coding. The capability of touching all aspects of the design, development, and maintenance of software systems is significant. ML models are capable of processing codebases on a large scale and learning about the structure as well as the style of coding. It also allows code completion tools that will be able to predict even a whole part of code or even generate it in the form of a template. For instance, the application of ML is evident in the way GitHub’s Copilot suggests contextually relevant code snippets that solve developers’ work by nearly half. What You'll Learn: Understand the basics of microservices and API design and see how AI can make these systems smarter and more efficient. Discover how to use AI in microservices and APIs to automate tasks, improve performance, and boost security. Learn how to design scalable and secure systems by following best practices and innovative approaches. Get practical tips on troubleshooting and solving challenges in AI-powered microservice architectures. Who is this book for: Software architects and engineers, AI and Machine Learning professionals, and DevOps engineers
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