The Co-Pilot Engineering: Building the Digital Second Brain

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Audiobook

Hardcover

Kindle

Paperback

Detalles del producto

Disponibilidad:
En stock

Peso con empaque:
0.91 kg

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No

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Nuevo

Producto de:
Amazon

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USA

Sobre este producto

The Co-Pilot Engineering: Building the Digital Second Brain" is a comprehensive, hands-on guide designed to equip students and professionals with the end-to-end skills required to design, build, and deploy sophisticated AI-powered Co-Pilots. In an era where generative AI is reshaping industries, this book provides the essential engineering roadmap to harness its power effectively. It bridges the critical gap between understanding AI theory and applying it to create practical, value-driven applications. Philosophy The core philosophy of this book is "Learning by Building." We believe that true mastery in an engineering discipline comes from practical application, not just passive absorption of information. The content is structured to be intensely practical, demystifying complex topics by breaking them down into manageable, understandable, and implementable components. I have simplified without sacrificing depth, ensuring that readers not only learn how to use a tool or framework but also understand the fundamental principles of why it works. The central theme is empowerment—transforming the reader from a consumer of AI technology into a creator of it. Key Features 1. Syllabus Compliant: Carefully architected to cover the syllabus requirements for B.Tech/M.Tech courses in Computer Science of major international university programs. 2. Focus on Orchestration Frameworks: Provides in-depth coverage of industry-standard frameworks like LangChain and LlamaIndex, which are essential for building modern LLM applications. 3. Practical Tooling: Emphasizes the use of modern, practical tools and technologies, including Python, FastAPI, Vector Databases (e.g., ChromaDB, Pinecone), Docker, and cloud platforms. To Whom This Book Is Addressed This book is written for a broad audience within the technology landscape: 1. Undergraduate (B.Tech) Students: Computer Science and IT students who want a practical, project-based introduction to building generative AI applications. 2. Postgraduate (M.Tech) Students: Students specializing in AI/ML or Software Engineering who need a guide to advanced application development. 3. Software Developers & Engineers: Professionals seeking to upskill and transition into the rapidly growing field of AI engineering. 4. AI/ML Practitioners: Data scientists and ML engineers who want to move beyond model training and into building robust, end-to-end AI systems.