Twardo ryein (17 résultats)

Paperback or Softback. Etat : New. Private AI with Spark: Design, package, and operate private AI locally using Apache Spark, batch pipelines, and vLLM acceleration. Book.

Etat : New.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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

Etat : New. Print on Demand.

Etat : New. Print on Demand.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 164 pp. Englisch.

Etat : New. PRINT ON DEMAND.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 256 pp. Englisch.

Paperback. Etat : new. Paperback. For those who want to build controlled, reproducible AI systems entirely within your own infrastructure, this book is the most practical and implementation-focused trainer. Instead of relying on external APIs or cloud-hosted intelligence services, this book clearly demonstrates how Apache Spark can orchestrate data preparation, model training, batch inference, reporting, and LLM acceleration in a disciplined and transparent way.As the book opens, it swiftly defines private AI, making it clear that external AI calls are not allowed, full ownership of datasets and model assets is imperative, and repeatable runs with traceable outputs are essential. I will use a realistic sample to show you how to build an end-to-end workflow that ingests raw data, normalizes it into a stable schema, trains a baseline classifier, extracts keywords, generates summaries, and produces structured reports. There's no doubt that each step is implemented with clarity and attention to maintainability. You can be sure that logging, manifests, and monitoring are embedded from the start. We implement classic machine learning techniques, vLLM, performance measurement, batch processing patterns, quarantine handling, and structured metrics to make private AI more usable and compete with cloud-based AI.Beyond experimentation, the book transitions seamlessly into packaging and routine execution. It will teach you to bundle multiple stages into a single command workflow, schedule daily or weekly runs, generate compact run reports, and adapt the architecture to new datasets without redesigning the system. It does not promise instant transformation or one-click AI solutions. Instead, it provides a structured path to building a sustainable private AI backbone using Spark as the orchestration layer.Key LearningsNo external AI calls and full control over data, models, and repeatable runs.Stable canonical schema with downstream ML and reusable reporting.Infuse Classic ML with Spark without introducing LLM complexity.Carry out extractive summaries without hallucination risk.Complete traceability through manifests, prompt versions, and run logs.Implement data and batch flow, along with fast inference using vLLM.Extract inspectable data and surface out the hidden errors using quarantine tables.Measure and store performance for every run with stakeholder reporting.Design single-command pipeline with clear configs to build repeatable AI.Table of ContentUp and Running with Private AIData Workflows using Spark DataFramesPowerful NLP without LLMBatch Inference and Practical OutputsSmart SummariesBoosting with vLLM IntegrationPackaging Private AI This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - For those who want to build controlled, reproducible AI systems entirely within your own infrastructure, this book is the most practical and implementation-focused trainer. Instead of relying on external APIs or cloud-hosted intelligence services, this book clearly demonstrates how Apache Spark can orchestrate data preparation, model training, batch inference, reporting, and LLM acceleration in a disciplined and transparent way.As the book opens, it swiftly defines private AI, making it clear that external AI calls are not allowed, full ownership of datasets and model assets is imperative, and repeatable runs with traceable outputs are essential. I will use a realistic sample to show you how to build an end-to-end workflow that ingests raw data, normalizes it into a stable schema, trains a baseline classifier, extracts keywords, generates summaries, and produces structured reports. There's no doubt that each step is implemented with clarity and attention to maintainability. You can be sure that logging, manifests, and monitoring are embedded from the start. We implement classic machine learning techniques, vLLM, performance measurement, batch processing patterns, quarantine handling, and structured metrics to make private AI more usable and compete with cloud-based AI.Beyond experimentation, the book transitions seamlessly into packaging and routine execution. It will teach you to bundle multiple stages into a single command workflow, schedule daily or weekly runs, generate compact run reports, and adapt the architecture to new datasets without redesigning the system. It does not promise instant transformation or one-click AI solutions. Instead, it provides a structured path to building a sustainable private AI backbone using Spark as the orchestration layer.Key LearningsNo external AI calls and full control over data, models, and repeatable runs.Stable canonical schema with downstream ML and reusable reporting.Infuse Classic ML with Spark without introducing LLM complexity.Carry out extractive summaries without hallucination risk.Complete traceability through manifests, prompt versions, and run logs.Implement data and batch flow, along with fast inference using vLLM.Extract inspectable data and surface out the hidden errors using quarantine tables.Measure and store performance for every run with stakeholder reporting.Design single-command pipeline with clear configs to build repeatable AI.Table of ContentUp and Running with Private AIData Workflows using Spark DataFramesPowerful NLP without LLMBatch Inference and Practical OutputsSmart SummariesBoosting with vLLM IntegrationPackaging Private AI.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Get practical hands-on to build your own knowledge graph, and use it across retrieval, analytics and agentsLLMs sound confident, but they forget context, invent facts and can't show their reasoning. A knowledge graph gives them structured, traceable facts to work from. What you'll do in this book is grow a single Neo4j graph from the Python Enhancement Proposals, which are the documents that shaped the language. It starts with getting to grips with nodes and relationships and writing Cypher like a pro. Then you design a schema in Pydantic, chunk documents into a lexical graph and extract entities with spaCy and local LLMs. Then, you add vector embeddings and indexes, and combine full-text search, vector search and graph traversal into GraphRAG retrieval that cites its sources.The rest of the book goes into more detail. You can rank and cluster nodes with Graph Data Science, infer new facts with rules, import ontologies and validate data with SHACL. You also give LangGraph agents guarded Cypher tools, serve the graph over MCP, store agent memory with Graphiti and trace every decision. At the end of the day, you keep the graph healthy with regular refreshes, time-bound facts, evaluation runs, query tuning and backups.Key LearningsWrite expressive, parameterized Cypher queries from Python.Design graph schemas with Pydantic, constraints and indexes.Chunk documents into lexical graphs with batched ingestion pipelines.Extract entities and relationships using spaCy rules and schema-guided local LLMs.Store embeddings in Neo4j and query them through vector indexes and similarity metrics.Build GraphRAG retrievers for full-text, vector search and subgraph expansion.Rank, cluster and predict links with PageRank, Leiden and GDS pipelines.Infer facts with Cypher rules, SKOS ontologies and SHACL shape validation.Connect LangGraph agents to graphs through guarded Cypher tools, MCP and Graphiti memory.Operate graphs with incremental refreshes, temporal facts, evaluation runs, tuning and backups.Table of ContentThinking in Nodes and RelationshipsWriting Cypher with ConfidenceDesigning Graph SchemaPreparing Data and ChunksExtracting Entities and RelationshipsStructuring Embeddings in GraphsRetrieving with Vectors and StructureAnalyzing Graph StructureReasoning Symbolically over KnowledgeConnecting AI Agents to GraphsKeeping Knowledge Graphs Healthy.…

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -For those who want to build controlled, reproducible AI systems entirely within your own infrastructure, this book is the most practical and implementation-focused trainer. Instead of relying on external APIs or cloud-hosted intelligence services, this book clearly demonstrates how Apache Spark can orchestrate data preparation, model training, batch inference, reporting, and LLM acceleration in a disciplined and transparent way.As the book opens, it swiftly defines private AI, making it clear that external AI calls are not allowed, full ownership of datasets and model assets is imperative, and repeatable runs with traceable outputs are essential. I will use a realistic sample to show you how to build an end-to-end workflow that ingests raw data, normalizes it into a stable schema, trains a baseline classifier, extracts keywords, generates summaries, and produces structured reports. There's no doubt that each step is implemented with clarity and attention to maintainability. You can be sure that logging, manifests, and monitoring are embedded from the start. We implement classic machine learning techniques, vLLM, performance measurement, batch processing patterns, quarantine handling, and structured metrics to make private AI more usable and compete with cloud-based AI.Beyond experimentation, the book transitions seamlessly into packaging and routine execution. It will teach you to bundle multiple stages into a single command workflow, schedule daily or weekly runs, generate compact run reports, and adapt the architecture to new datasets without redesigning the system. It does not promise instant transformation or one-click AI solutions. Instead, it provides a structured path to building a sustainable private AI backbone using Spark as the orchestration layer.Key LearningsNo external AI calls and full control over data, models, and repeatable runs.Stable canonical schema with downstream ML and reusable reporting.Infuse Classic ML with Spark without introducing LLM complexity.Carry out extractive summaries without hallucination risk.Complete traceability through manifests, prompt versions, and run logs.Implement data and batch flow, along with fast inference using vLLM.Extract inspectable data and surface out the hidden errors using quarantine tables.Measure and store performance for every run with stakeholder reporting.Design single-command pipeline with clear configs to build repeatable AI.Table of ContentUp and Running with Private AIData Workflows using Spark DataFramesPowerful NLP without LLMBatch Inference and Practical OutputsSmart SummariesBoosting with vLLM IntegrationPackaging Private AILibri GmbH, Europaallee 1, 36244 Bad Hersfeld 164 pp. Englisch.…

Taschenbuch. Etat : Neu. Private AI with Spark | Design, package, and operate private AI locally using Apache Spark, batch pipelines, and vLLM acceleration | Ryein Twardo | Taschenbuch | Englisch | 2026 | GitforGits | EAN 9789349174160 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Get practical hands-on to build your own knowledge graph, and use it across retrieval, analytics and agentsLLMs sound confident, but they forget context, invent facts and can't show their reasoning. A knowledge graph gives them structured, traceable facts to work from. What you'll do in this book is grow a single Neo4j graph from the Python Enhancement Proposals, which are the documents that shaped the language. It starts with getting to grips with nodes and relationships and writing Cypher like a pro. Then you design a schema in Pydantic, chunk documents into a lexical graph and extract entities with spaCy and local LLMs. Then, you add vector embeddings and indexes, and combine full-text search, vector search and graph traversal into GraphRAG retrieval that cites its sources.The rest of the book goes into more detail. You can rank and cluster nodes with Graph Data Science, infer new facts with rules, import ontologies and validate data with SHACL. You also give LangGraph agents guarded Cypher tools, serve the graph over MCP, store agent memory with Graphiti and trace every decision. At the end of the day, you keep the graph healthy with regular refreshes, time-bound facts, evaluation runs, query tuning and backups.Key LearningsWrite expressive, parameterized Cypher queries from Python.Design graph schemas with Pydantic, constraints and indexes.Chunk documents into lexical graphs with batched ingestion pipelines.Extract entities and relationships using spaCy rules and schema-guided local LLMs.Store embeddings in Neo4j and query them through vector indexes and similarity metrics.Build GraphRAG retrievers for full-text, vector search and subgraph expansion.Rank, cluster and predict links with PageRank, Leiden and GDS pipelines.Infer facts with Cypher rules, SKOS ontologies and SHACL shape validation.Connect LangGraph agents to graphs through guarded Cypher tools, MCP and Graphiti memory.Operate graphs with incremental refreshes, temporal facts, evaluation runs, tuning and backups.Table of ContentThinking in Nodes and RelationshipsWriting Cypher with ConfidenceDesigning Graph SchemaPreparing Data and ChunksExtracting Entities and RelationshipsStructuring Embeddings in GraphsRetrieving with Vectors and StructureAnalyzing Graph StructureReasoning Symbolically over KnowledgeConnecting AI Agents to GraphsKeeping Knowledge Graphs Healthy 256 pp. Englisch.…

Taschenbuch. Etat : Neu. Programming Knowledge Graphs with Python | Model, Build, and Query AI-Ready Knowledge with Cypher, Neo4j, and AI Agents | Ryein Twardo | Taschenbuch | Englisch | 2026 | GitforGits | EAN 9788117136096 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…