Limited Time SaleUS$27.00 cheaper than the new price!!
| Management number | 220802543 | Release Date | 2026/05/03 | List Price | US$18.00 | Model Number | 220802543 | ||
|---|---|---|---|---|---|---|---|---|---|
| Category | |||||||||
Build Production-Ready Generative AI Agents Systems from Architecture to Deployment with This BookAutonomous AI agents are transforming how organizations operate—from customer service to enterprise workflows—yet comprehensive resources for mastering agentic AI systems and preparing for related certifications remain scarce.Volume 2 bridges theory and production by diving into deployment architectures and advanced reasoning techniques that transform conceptual agents into scalable systems. This volume covers multi-agent orchestration frameworks (LangChain, LangGraph, AutoGen, CrewAI), NVIDIA's deployment stack including NeMo Guardrails and NIM inference optimization, systematic performance evaluation through benchmarking and A/B testing, and sophisticated reasoning approaches for complex problem-solving.The Only Comprehensive NCP-AAI Certification GuideUnlike generic AI textbooks, this book series is the only comprehensive resource aligned to the NVIDIA Certified Professional - Agentic AI (NCP-AAI) exam blueprint. It combines rigorous exam preparation with production-focused implementation skills, giving you both the credentials employers seek and the capabilities to deploy autonomous systems that deliver measurable business value.Critical Skills You'll Build:Architect and implement multi-agent systems with memory, perception, and reasoning capabilities that handle real-world complexityDevelop production-ready agents using industry-standard frameworks (LangChain, LangGraph, AutoGen, CrewAI, etc.) with code examplesDeploy and scale autonomous systems using NVIDIA's technology stack: NeMo Guardrails for safety, NIM for optimized inference, Agent Toolkit for orchestration, and so onSystematically evaluate agent performance through benchmarking pipelines, A/B testing frameworks, and multi-objective optimization techniquesImplement enterprise-grade safety, including guardrails, Constitutional AI principles, RLHF tuning, and human-in-the-loop workflowsExpert Guidance from an NVIDIA Certified ProfessionalThe author holds the NVIDIA Certified Professional - Agentic AI credential with 9+ years of AI Research and Development experience at the US Federal Reserve Bank and Federal Government. As a Certified Information System Security Professional (CISSP) with published cybersecurity research on AI systems, the author currently serves as CEO of a nonprofit organization at the intersection of AI and cybersecurity while contributing as a research reviewer for Tier 1 journals in Computer Science and Digital Health. This unique combination of certification expertise, production experience, and security background ensures you learn both cutting-edge techniques and battle-tested deployment patterns.Who This Book ServesWritten for AI professionals, software engineers, and technical leads with Python and basic ML background, this guide assumes no prior agent experience. Whether you're preparing for the NVIDIA agentic AI certification or building production autonomous systems, you'll gain immediately applicable skills for enterprise deployment.Proven Learning Through PracticeEvery chapter follows the "I Do, We Do, You Do" pedagogy: conceptual foundations with worked examples, guided practice through implementation exercises, and independent mastery validated through comprehensive quizzes. With 96 focused sections across 10 parts, complete production code, hands-on labs, and over 3,000 certification-aligned practice questions, you'll build confidence through repetition and validation. Read more
| ISBN13 | 979-8247404286 |
|---|---|
| Language | English |
| Publisher | Independently published |
| Dimensions | 8 x 1.4 x 10 inches |
| Item Weight | 3.29 pounds |
| Reading age | 15 - 18 years |
| Print length | 620 pages |
| Publication date | February 19, 2026 |
If you notice any omissions or errors in the product information on this page, please use the correction request form below.
Correction Request Form