Vector Database Deep Dive: Optimize AI Workflows for Speed, Accuracy, and Enterprise Scale (The AI Developer Series) - Softcover
Book 4 of 5: The AI Developer Series
Synopsis
Vector Database Deep Dive: Optimize AI Workflows for Speed, Accuracy, and Enterprise Scale
Why do some AI systems scale effortlessly while others collapse under pressure? Why do high-performing models still return irrelevant results? The answer often lies not in the models—but in the databases powering them.
Vector Database Deep Dive confronts one of the most pressing challenges in AI engineering today: how to manage high-dimensional data at speed and scale without compromising precision. This book delivers a technical blueprint for professionals and teams who want to harness the full potential of vector databases to accelerate retrieval-augmented generation (RAG), improve semantic search, and streamline end-to-end machine learning workflows.
Built on real-world use cases and production-ready practices, this book equips you with a modern, system-level understanding of how vector databases drive AI performance. Whether you're building intelligent chat systems, scaling recommendation engines, or supporting multimodal embeddings, you’ll learn how to architect, optimize, and integrate vector stores for maximum impact.
Inside, you’ll master:
Structuring high-dimensional data for fast approximate nearest neighbor (ANN) search
Indexing and filtering strategies for hybrid retrieval at scale
Real-time ingestion, chunking, and embedding workflows with tools like FAISS, Qdrant, Milvus, Weaviate, and Elasticsearch
Vector store evaluation frameworks for latency, recall, and throughput
Memory-augmented applications and context window optimization using vector-backed architectures
Scaling strategies for production deployments, from fine-tuning ingestion pipelines to sharding and horizontal scaling
You won’t just gain theory—you’ll build, deploy, and optimize live vector-based systems from the ground up, with clear code examples and deployment scenarios.
If you're an AI engineer, data architect, or software developer responsible for production ML systems, this book delivers the hands-on frameworks, mental models, and best practices you need to lead in the AI era.
"synopsis" may belong to another edition of this title.
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Paperback. Condition: new. Paperback. Vector Database Deep Dive: Optimize AI Workflows for Speed, Accuracy, and Enterprise ScaleWhy do some AI systems scale effortlessly while others collapse under pressure? Why do high-performing models still return irrelevant results? The answer often lies not in the models-but in the databases powering them.Vector Database Deep Dive confronts one of the most pressing challenges in AI engineering today: how to manage high-dimensional data at speed and scale without compromising precision. This book delivers a technical blueprint for professionals and teams who want to harness the full potential of vector databases to accelerate retrieval-augmented generation (RAG), improve semantic search, and streamline end-to-end machine learning workflows.Built on real-world use cases and production-ready practices, this book equips you with a modern, system-level understanding of how vector databases drive AI performance. Whether you're building intelligent chat systems, scaling recommendation engines, or supporting multimodal embeddings, you'll learn how to architect, optimize, and integrate vector stores for maximum impact.Inside, you'll master: Structuring high-dimensional data for fast approximate nearest neighbor (ANN) searchIndexing and filtering strategies for hybrid retrieval at scaleReal-time ingestion, chunking, and embedding workflows with tools like FAISS, Qdrant, Milvus, Weaviate, and ElasticsearchVector store evaluation frameworks for latency, recall, and throughputMemory-augmented applications and context window optimization using vector-backed architecturesScaling strategies for production deployments, from fine-tuning ingestion pipelines to sharding and horizontal scalingYou won't just gain theory-you'll build, deploy, and optimize live vector-based systems from the ground up, with clear code examples and deployment scenarios.If you're an AI engineer, data architect, or software developer responsible for production ML systems, this book delivers the hands-on frameworks, mental models, and best practices you need to lead in the AI era. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798296370396
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Paperback. Condition: new. Paperback. Vector Database Deep Dive: Optimize AI Workflows for Speed, Accuracy, and Enterprise ScaleWhy do some AI systems scale effortlessly while others collapse under pressure? Why do high-performing models still return irrelevant results? The answer often lies not in the models-but in the databases powering them.Vector Database Deep Dive confronts one of the most pressing challenges in AI engineering today: how to manage high-dimensional data at speed and scale without compromising precision. This book delivers a technical blueprint for professionals and teams who want to harness the full potential of vector databases to accelerate retrieval-augmented generation (RAG), improve semantic search, and streamline end-to-end machine learning workflows.Built on real-world use cases and production-ready practices, this book equips you with a modern, system-level understanding of how vector databases drive AI performance. Whether you're building intelligent chat systems, scaling recommendation engines, or supporting multimodal embeddings, you'll learn how to architect, optimize, and integrate vector stores for maximum impact.Inside, you'll master: Structuring high-dimensional data for fast approximate nearest neighbor (ANN) searchIndexing and filtering strategies for hybrid retrieval at scaleReal-time ingestion, chunking, and embedding workflows with tools like FAISS, Qdrant, Milvus, Weaviate, and ElasticsearchVector store evaluation frameworks for latency, recall, and throughputMemory-augmented applications and context window optimization using vector-backed architecturesScaling strategies for production deployments, from fine-tuning ingestion pipelines to sharding and horizontal scalingYou won't just gain theory-you'll build, deploy, and optimize live vector-based systems from the ground up, with clear code examples and deployment scenarios.If you're an AI engineer, data architect, or software developer responsible for production ML systems, this book delivers the hands-on frameworks, mental models, and best practices you need to lead in the AI era. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798296370396
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