Ai engineering for practitioners (29 risultati)

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Paperback. Condizione: New.

Paperback. Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: As New. Unread book in perfect condition.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: New.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New.

Paperback. Condizione: New.

Paperback. Condizione: new. Paperback. AI systems are evolving beyond simple chatbots. Cognitive agents can remember, reason, and adapt, but they only succeed if context is engineered correctly. Without the right memory pipelines, retrieval strategies, and safeguards, even the most powerful language models fail to deliver.Context Engineering for Cognitive Agents is the first technical guide dedicated to solving this challenge. It provides developers, researchers, and innovators with a complete framework for designing agents that use context effectively to think, learn, and act.Inside, you will learn how to: Understand context windows, memory types, and retrieval strategiesBuild memory architectures that extend beyond LLM limitsImplement Retrieval-Augmented Generation (RAG) with vector databasesApply summarization and compression to manage context efficientlyOrchestrate reasoning and context pipelines for complex tasksCompare frameworks like LangChain, LlamaIndex, AutoGen, and CrewAISecure your agents against context injection and adversarial promptsExplore case studies of cognitive agents in research, support, and automationWhether you are building next-generation applications, designing multi-agent systems, or researching the limits of AI cognition, this book equips you with the tools and practices to move from fragile prototypes to reliable, context-aware systems.If you are ready to master the missing piece of AI agent engineering, this book is your essential guide. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New. Print on Demand.

Paperback. Condizione: New.

Paperback. Condizione: new. Paperback. Vector Database Engineering is the ultimate guide to designing, building, and deploying scalable vector search systems using tools like FAISS, Milvus, Pinecone, Weaviate, and Qdrant. Whether you're building a semantic search engine, a personalized recommendation system, or an AI-powered chatbot, this book gives you the theoretical foundations, mathematical insights, and production-ready Python code you need to succeed.What You'll LearnVector Embeddings & Similarity Search: Represent text, images, and data as vectors and retrieve results using cosine, Euclidean, and inner product distances.Vector Indexing at Scale: Implement FAISS HNSW, IVF, and PQ structures. Learn trade-offs between recall and latency.Managed & Distributed Databases: Use managed services like Pinecone and self-hosted options like Milvus, Weaviate, and Qdrant.Real-World Applications: Build semantic search engines, RAG pipelines, multimodal retrieval, recommendation systems, and edge deployments.Security & Compliance: Add RBAC, TLS encryption, audit logging, and GDPR-compliant deletion.Advanced Topics: Explore neural search, adaptive indexing, multimodal embeddings (e.g., CLIP), and federated search.Key Use CasesSemantic Search: Go beyond keywords using AI vector queries.Recommendations: Suggest content and products based on behavior.Multimedia Retrieval: Search images, audio, and video using embeddings.RAG: Feed live vector data into LLMs for better answers.Fraud & Anomaly Detection: Identify outliers with proximity-based search.NLP & Generative AI: Embed, retrieve, and generate content with LLMs.Why This Book?Hands-On Python: 40+ real-world examples with FAISS, Qdrant, Pinecone, Milvus, and Weaviate.Math-Based Optimization: Understand latency, memory, and performance trade-offs.Production Ready: Secure, scalable design patterns with best practices.Future Trends: Includes neural retrievers, adaptive indexing, and multimodal workflows.Who It's ForEngineers building real-time search and recommendation enginesML and Data Scientists integrating vector search in pipelinesDevOps deploying scalable and secure AI infrastructureAI researchers exploring retrieval-augmented generationStudents and builders learning practical vector searchThis is your in-depth, code-first guide to building intelligent, scalable vector database systems. Start using vector search to power the next generation of AI.Get your copy now. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Paperback. Condizione: new. Paperback. The rise of AI-powered development tools has changed the way we write software. For the first time, developers can work alongside an AI partner that doesn't just autocomplete lines of code, but understands context, explains reasoning, and adapts to the way you build. I identified that Claude Code represents a major step forward in this space, not as a replacement for developers, but as a true collaborator.This book was written for Python developers who want to get practical with Claude Code. It doesn't assume you're here for hype or marketing. Instead, it focuses on real workflows: setting up the Claude API, writing prompts that produce usable code, debugging and refactoring with AI assistance, and deploying applications where Claude helps handle the heavy lifting. You'll find examples, code snippets, and case studies that can be dropped into your own projects immediately.As developers, we know the value of tools that make us faster without compromising quality. Claude Code does that when you learn how to guide it effectively. The lessons in these pages are the result of hands-on testing, trial and error, and a focus on what works in practice. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. AI systems are evolving beyond simple chatbots. Cognitive agents can remember, reason, and adapt, but they only succeed if context is engineered correctly. Without the right memory pipelines, retrieval strategies, and safeguards, even the most powerful language models fail to deliver.Context Engineering for Cognitive Agents is the first technical guide dedicated to solving this challenge. It provides developers, researchers, and innovators with a complete framework for designing agents that use context effectively to think, learn, and act.Inside, you will learn how to: Understand context windows, memory types, and retrieval strategiesBuild memory architectures that extend beyond LLM limitsImplement Retrieval-Augmented Generation (RAG) with vector databasesApply summarization and compression to manage context efficientlyOrchestrate reasoning and context pipelines for complex tasksCompare frameworks like LangChain, LlamaIndex, AutoGen, and CrewAISecure your agents against context injection and adversarial promptsExplore case studies of cognitive agents in research, support, and automationWhether you are building next-generation applications, designing multi-agent systems, or researching the limits of AI cognition, this book equips you with the tools and practices to move from fragile prototypes to reliable, context-aware systems.If you are ready to master the missing piece of AI agent engineering, this book is your essential guide. 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. Condizione: new. Paperback. Vector Database Engineering is the ultimate guide to designing, building, and deploying scalable vector search systems using tools like FAISS, Milvus, Pinecone, Weaviate, and Qdrant. Whether you're building a semantic search engine, a personalized recommendation system, or an AI-powered chatbot, this book gives you the theoretical foundations, mathematical insights, and production-ready Python code you need to succeed.What You'll LearnVector Embeddings & Similarity Search: Represent text, images, and data as vectors and retrieve results using cosine, Euclidean, and inner product distances.Vector Indexing at Scale: Implement FAISS HNSW, IVF, and PQ structures. Learn trade-offs between recall and latency.Managed & Distributed Databases: Use managed services like Pinecone and self-hosted options like Milvus, Weaviate, and Qdrant.Real-World Applications: Build semantic search engines, RAG pipelines, multimodal retrieval, recommendation systems, and edge deployments.Security & Compliance: Add RBAC, TLS encryption, audit logging, and GDPR-compliant deletion.Advanced Topics: Explore neural search, adaptive indexing, multimodal embeddings (e.g., CLIP), and federated search.Key Use CasesSemantic Search: Go beyond keywords using AI vector queries.Recommendations: Suggest content and products based on behavior.Multimedia Retrieval: Search images, audio, and video using embeddings.RAG: Feed live vector data into LLMs for better answers.Fraud & Anomaly Detection: Identify outliers with proximity-based search.NLP & Generative AI: Embed, retrieve, and generate content with LLMs.Why This Book?Hands-On Python: 40+ real-world examples with FAISS, Qdrant, Pinecone, Milvus, and Weaviate.Math-Based Optimization: Understand latency, memory, and performance trade-offs.Production Ready: Secure, scalable design patterns with best practices.Future Trends: Includes neural retrievers, adaptive indexing, and multimodal workflows.Who It's ForEngineers building real-time search and recommendation enginesML and Data Scientists integrating vector search in pipelinesDevOps deploying scalable and secure AI infrastructureAI researchers exploring retrieval-augmented generationStudents and builders learning practical vector searchThis is your in-depth, code-first guide to building intelligent, scalable vector database systems. Start using vector search to power the next generation of AI.Get your copy now. 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. Condizione: new. Paperback. The rise of AI-powered development tools has changed the way we write software. For the first time, developers can work alongside an AI partner that doesn't just autocomplete lines of code, but understands context, explains reasoning, and adapts to the way you build. I identified that Claude Code represents a major step forward in this space, not as a replacement for developers, but as a true collaborator.This book was written for Python developers who want to get practical with Claude Code. It doesn't assume you're here for hype or marketing. Instead, it focuses on real workflows: setting up the Claude API, writing prompts that produce usable code, debugging and refactoring with AI assistance, and deploying applications where Claude helps handle the heavy lifting. You'll find examples, code snippets, and case studies that can be dropped into your own projects immediately.As developers, we know the value of tools that make us faster without compromising quality. Claude Code does that when you learn how to guide it effectively. The lessons in these pages are the result of hands-on testing, trial and error, and a focus on what works in practice. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…