AI Engineering 2026: Production-Grade Applications with Frontier Models (eBook, ePUB)
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AI Engineering 2026: Production-Grade Applications with Frontier ModelsBuild Real-World, Scalable Systems with Frontier AIAI Engineering 2026 is the definitive guide for engineers, architects, and technical leaders who need to move beyond experimentation and build production-grade AI systems using modern frontier models.As generative AI rapidly transforms software development, the challenge is no longer "Can we use AI?" but "How do we engineer AI systems that scale reliably, safely, and economically-now and in 2026?"This book gives you the frameworks, tools, and engineering patterns required t...
AI Engineering 2026: Production-Grade Applications with Frontier Models
Build Real-World, Scalable Systems with Frontier AI
AI Engineering 2026 is the definitive guide for engineers, architects, and technical leaders who need to move beyond experimentation and build production-grade AI systems using modern frontier models.
As generative AI rapidly transforms software development, the challenge is no longer "Can we use AI?" but "How do we engineer AI systems that scale reliably, safely, and economically-now and in 2026?"
This book gives you the frameworks, tools, and engineering patterns required to ship real, enterprise-ready AI applications.
What You Will Learn
Who This Book Is For
Whether you're building copilots, search systems, agent frameworks, or vertical AI applications, this book gives you the clear engineering patterns needed to go from prototype to production-grade reality.
Why This Book Matters
Frontier AI in 2026 is not just about models-it's about engineering. The organizations that win are those that can build robust systems that scale, integrate, evolve, and operate continuously.
AI Engineering 2026 provides the practical guidance, architectural patterns, and operational playbooks needed to succeed in the next era of software.
Build Real-World, Scalable Systems with Frontier AI
AI Engineering 2026 is the definitive guide for engineers, architects, and technical leaders who need to move beyond experimentation and build production-grade AI systems using modern frontier models.
As generative AI rapidly transforms software development, the challenge is no longer "Can we use AI?" but "How do we engineer AI systems that scale reliably, safely, and economically-now and in 2026?"
This book gives you the frameworks, tools, and engineering patterns required to ship real, enterprise-ready AI applications.
What You Will Learn
- AI Systems Architecture Design modular, resilient architectures built around frontier models, retrieval systems, and distributed inference.
- Production-Grade LLM Engineering Apply best practices for model orchestration, evaluation, latency optimization, and model monitoring.
- Frontier Model Integration Build with multi-modal, agentic, and reasoning models while ensuring safety, performance, and maintainability.
- Retrieval & Knowledge Engineering Master vector databases, retrieval pipelines, knowledge graphs, embeddings, and hybrid search patterns.
- AI Deployment & Scalability Implement cost-efficient inference, autoscaling strategies, caching layers, and GPU/TPU optimization.
- Safety, Governance & Risk Controls Integrate system-level guardrails, red-teaming techniques, observability, and responsible AI controls.
- Agent Systems & Workflow Automation Build reliable multi-agent workflows with memory, planning, tool use, and human-in-the-loop protocols.
Who This Book Is For
- Software engineers building AI-powered products
- ML practitioners deploying real-world LLM systems
- Engineering managers and solution architects
- Technical founders developing AI-first startups
- Enterprise teams modernizing existing applications with AI
Whether you're building copilots, search systems, agent frameworks, or vertical AI applications, this book gives you the clear engineering patterns needed to go from prototype to production-grade reality.
Why This Book Matters
Frontier AI in 2026 is not just about models-it's about engineering. The organizations that win are those that can build robust systems that scale, integrate, evolve, and operate continuously.
AI Engineering 2026 provides the practical guidance, architectural patterns, and operational playbooks needed to succeed in the next era of software.
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