دانلود کتاب هوش مصنوعی کاربردی برای توسعه جاوا با کمک هوش مصنوعی مولد، LLMها و یادگیری ماشین

The demand for artificial intelligence (AI) skills in the enterprise Java world is exploding, but let’s face it: learning AI can be intimidating for Java developers. Many resources are too theoretical, focus heavily on data science, or rely on programming languages that are unfamiliar to enterprise environments. As seasoned programmers with years of experience in large-scale enterprise Java projects, we’ve faced the same challenges. When we started exploring AI and large language models (LLMs), we were frustrated by the lack of practical resources tailored to Java developers. Most materials seemed out of reach, buried under layers of Python code and abstract concepts. That’s why we wrote this book. It’s the practical guide we wish we had, designed for Java developers who want to build real-world AI applications using the tools and frameworks they already know and love. Inside, you’ll find clear explanations of essential AI techniques, hands-on examples, and real-world projects that will help you integrate AI into your existing Java projects. When we started circulating the early draft release of this book, we quickly received a ton of excitement. But one comment stuck with me that went along the lines of: “Everybody is an AI expert these days, and unless you have 10 years of experience as a data scientist, you should not write a book like this.” That strong sentence automatically raised the imposter syndrome in all of us. But it also gave us an opportunity to reiterate why we wanted to write this book and share our view on enterprise application development in these times of AI with you. We have seen a lot of enterprises starting to infuse AI into existing applications. Businesses are eager to quickly use AI features to enhance user experience, optimize and automate workflows, and speed up operations. However, the velocity of this push often leads to fragile implementations. Many organizations find themselves deploying systems cobbled together from prototypes, typically using scripting languages and notebooks. These solutions often lack the scalability, governance, and resilience demanded by production environments. We believe that a strategic, robust foundation for enterprise AI isn’t necessarily a completely new technology stack. As a matter of fact, most enterprises already possess and trust their Java runtimes with the most complex and governed processes you can imagine. We also believe that the speed of experimentation offered by some tools doesn’t equate to the speed and reliability needed for production environments. Modern, cloud native Java, with frameworks like Quarkus and LangChain4j, provides the necessary foundation to build observable, secure, and resilient AI-infused applications that operate effectively on Kubernetes environments, grounded in sound engineering principles. But it is also true that building an AI-infused application extends far beyond typical application development. A production-ready AI feature encompasses a complete system. Scripting languages like Python excel during the discovery and model-training phases. However, they often introduce fragility when used as the core runtime for the entire AI-infused application in production. Production AI is not a model running in isolation. It’s an integrated network of components within mission-critical workflows, likely deployed on platforms like Kubernetes, and subject to the same stringent nonfunctional requirements as any other enterprise service. The rapid pace of AI advancements forces organizations to operationalize the quickest path from prototype to deployment. This often involves packaging Python scripts or notebooks into containers and integrating them into existing continuous integration / continuous delivery (CI/CD) pipelines. This approach introduces significant risks and potentially uses shortcuts that inevitably accrue technical debt. And this is when one important question comes up more and more often: do formal standards still matter? The pragmatic answer is yes. Standards aren’t relics of a slower past; they are the often unseen infrastructure, enabling sustainable speed, quality, and innovation.