Hybrid AI Systems: Merging Small and Large Language Models for Efficient Intelligence: A Practical Guide to Task Orchestration, Model Optimization, Edge Deployment, and Scalable Production - Softcover

Inhaltsangabe

Unlock the Future of Artificial Intelligence with Hybrid AI Systems: Merging Small and Large Language Models for Efficient Intelligence
This definitive guide empowers engineers, AI practitioners, and researchers to design and deploy cost-efficient, scalable, and secure AI applications that seamlessly integrate Small Language Models (SLMs) and Large Language Models (LLMs). By combining the agility of lightweight SLMs with the deep reasoning of LLMs, hybrid AI systems deliver context-aware intelligence across edge devices and cloud environments, balancing performance, cost, and sustainability.
Dive into a practical roadmap for building hybrid AI ecosystems, packed with actionable insights and hands-on tools. Learn to orchestrate tasks using confidence-based routing, escalation triggers, and telemetry-driven workflows. Master model distillation and compression to optimize SLMs for edge hardware while leveraging LLMs for complex reasoning via secure cloud APIs. From Docker containerization and Kubernetes orchestration to monitoring dashboards with Prometheus and Grafana, this book equips you with production-ready blueprints, Python code samples, and FastAPI integrations. Whether you are deploying real-time assistants on constrained devices or architecting enterprise-scale AI systems, this guide bridges efficiency with innovation.

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