Miller Zhao (20 results)

Unknown. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Paperback. Condition: Brand New. 140 pages. 9.69x7.44x0.32 inches. In Stock.

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New.

Paperback. Condition: New.

Paperback. Condition: Very Good+. Color Illustrations; Square 8vo 8" to 9" tall; 25 pages; 1980 People's Publishing House of Fine Arts, Beijing. Square format paperback in color pictorial flaps with saddle stapled binding. 1st edition. Illustrated from color art by Grambs Miller and Zhao Long Yi. Text in both Chinese and English. Just trace shelf evidence to cover edges. Uncommon. VG++.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: New.

Paperback. Condition: new. Paperback. A professional, applied manual for designing, building, and operationalizing knowledge graphs that materially improve LLM-driven systems. This book provides the end-to-end technical roadmap data ingestion, entity/relation extraction, schema design, storage, querying, and LLM integration required to create explainable, high-precision hybrid AI systems.What's insideFundamentals of graph modeling, schema & ontology design, and graph theory essentials.Practical pipelines for extracting structured facts from unstructured text using NLP and embeddings.Integration patterns for Neo4j/RDF/graph stores, vector databases, and RAG architectures.Querying and analytics: SPARQL, Cypher, and hybrid retrieval approaches.Performance optimization, versioning, governance, and visualization techniques.Domain case studies (healthcare, finance, enterprise search) demonstrating measurable ROI.Key topics;knowledge graphs, graph databases, ontology design, entity extraction, SPARQL, Cypher, RAG, embeddings, semantic search, graph-augmented LLMs, information retrieval, data governance.Who should read thisData engineers, knowledge engineers, ML/AI practitioners, and technical product managers tasked with building authoritative retrieval systems or explainable AI features. A working knowledge of databases and basic NLP is helpful.Deliverables & formatReproducible projects that convert raw text into production-ready graph assets.Query recipes, integration blueprints, and operational guidelines for graph maintenance and scaling. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: new. Paperback. A rigorous, project-driven engineering guide to designing, implementing, and operating autonomous multi-agent systems. This book moves beyond theory to deliver production-grade patterns, tested code examples, and deployment strategies that enable teams to build resilient, observable, and secure agentic AI solutions.What's insideConcise theoretical foundations for agentic AI and multi-agent coordination.Architectural patterns for agent communication, role assignment, and decision policies.End-to-end Python implementations and reproducible projects (business automation, conversational agents, orchestrated pipelines).Engineering concerns: state management, retries, fault tolerance, monitoring, logging, and observability.Integration strategies for external APIs, databases, and vector stores.Security, compliance, and production hardening guidance.Key topics;agentic AI, multi-agent systems, autonomous agents, orchestration, workflow automation, agent communication, decision policies, fault tolerance, observability, Python, API integration, production scaling.Who should read thisSoftware engineers, ML engineers, platform architects, and technical leads building multi-step LLM workflows or autonomous pipelines that must operate reliably in production. Prior experience with Python and basic ML/LLM concepts is recommended.Deliverables & formatPractical code examples and templates ready for integration into production codebases.Two complete case studies with architecture diagrams and operational checklists.Best-practice playbooks for testing, deployment, incident response, and scaling. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condition: New.

Paperback. Condition: new. Paperback. A rigorous, project-driven engineering guide to designing, implementing, and operating autonomous multi-agent systems. This book moves beyond theory to deliver production-grade patterns, tested code examples, and deployment strategies that enable teams to build resilient, observable, and secure agentic AI solutions.What's insideConcise theoretical foundations for agentic AI and multi-agent coordination.Architectural patterns for agent communication, role assignment, and decision policies.End-to-end Python implementations and reproducible projects (business automation, conversational agents, orchestrated pipelines).Engineering concerns: state management, retries, fault tolerance, monitoring, logging, and observability.Integration strategies for external APIs, databases, and vector stores.Security, compliance, and production hardening guidance.Key topics;agentic AI, multi-agent systems, autonomous agents, orchestration, workflow automation, agent communication, decision policies, fault tolerance, observability, Python, API integration, production scaling.Who should read thisSoftware engineers, ML engineers, platform architects, and technical leads building multi-step LLM workflows or autonomous pipelines that must operate reliably in production. Prior experience with Python and basic ML/LLM concepts is recommended.Deliverables & formatPractical code examples and templates ready for integration into production codebases.Two complete case studies with architecture diagrams and operational checklists.Best-practice playbooks for testing, deployment, incident response, and scaling. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: new. Paperback. A professional, applied manual for designing, building, and operationalizing knowledge graphs that materially improve LLM-driven systems. This book provides the end-to-end technical roadmap data ingestion, entity/relation extraction, schema design, storage, querying, and LLM integration required to create explainable, high-precision hybrid AI systems.What's insideFundamentals of graph modeling, schema & ontology design, and graph theory essentials.Practical pipelines for extracting structured facts from unstructured text using NLP and embeddings.Integration patterns for Neo4j/RDF/graph stores, vector databases, and RAG architectures.Querying and analytics: SPARQL, Cypher, and hybrid retrieval approaches.Performance optimization, versioning, governance, and visualization techniques.Domain case studies (healthcare, finance, enterprise search) demonstrating measurable ROI.Key topics;knowledge graphs, graph databases, ontology design, entity extraction, SPARQL, Cypher, RAG, embeddings, semantic search, graph-augmented LLMs, information retrieval, data governance.Who should read thisData engineers, knowledge engineers, ML/AI practitioners, and technical product managers tasked with building authoritative retrieval systems or explainable AI features. A working knowledge of databases and basic NLP is helpful.Deliverables & formatReproducible projects that convert raw text into production-ready graph assets.Query recipes, integration blueprints, and operational guidelines for graph maintenance and scaling. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.