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Vision-Language Models in Production: Architecting Multimodal LLM Applications: From Vision-Language API to Self-Hosted Model (Production AI Engineering Series) - Softcover
Buch 16 von 21: Production AI Engineering Series
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The Engineer's Definitive Guide to Deploying Multimodal AI
Vision-language models (VLMs) represent the fastest-growing sector of AI deployment. While GPT-4o, Gemini 1.5 Pro, and open-source breakthroughs like LLaVA and InternVL2 have made multimodal AI a reality, transitioning from an API demo to a robust production system remains a monumental challenge. This book bridges that gap, offering a hands-on, engineering-first approach to shipping VLM-powered products.
What you will master inside:- Architectural Fundamentals: Go deep into visual encoders, projection layers, and language models to optimize prompt engineering and mitigate unexpected failure modes.
- Model Selection Frameworks: Learn how to weigh proprietary APIs against open-source alternatives based on latency, cost, privacy, and performance benchmarks.
- Production-Grade Preprocessing: Build highly efficient image pipelines that cut token consumption without sacrificing critical visual intelligence.
- Multimodal RAG: Design and scale retrieval systems that handle complex corpora containing both structured text and rich visual data.
- Rigorous Evaluation: Implement frameworks like LMMs-Eval and define production metrics to continuously monitor model behavior.
- Scalable Infrastructure & Serving: Master batching, caching, rate-limiting, and cost-management techniques tailored specifically for heavy multimodal workloads.
Whether you are building document processing pipelines, visual search engines, or autonomous agent frameworks, Vision-Language Models in Production provides the blueprints, patterns, and source-code insights needed to build resilient, cost-effective, and secure AI systems.
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Vision-Language Models in Production (Paperback)
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Paperback. Zustand: new. Paperback. The Engineer's Definitive Guide to Deploying Multimodal AIVision-language models (VLMs) represent the fastest-growing sector of AI deployment. While GPT-4o, Gemini 1.5 Pro, and open-source breakthroughs like LLaVA and InternVL2 have made multimodal AI a reality, transitioning from an API demo to a robust production system remains a monumental challenge. This book bridges that gap, offering a hands-on, engineering-first approach to shipping VLM-powered products.What you will master inside: Architectural Fundamentals: Go deep into visual encoders, projection layers, and language models to optimize prompt engineering and mitigate unexpected failure modes.Model Selection Frameworks: Learn how to weigh proprietary APIs against open-source alternatives based on latency, cost, privacy, and performance benchmarks.Production-Grade Preprocessing: Build highly efficient image pipelines that cut token consumption without sacrificing critical visual intelligence.Multimodal RAG: Design and scale retrieval systems that handle complex corpora containing both structured text and rich visual data.Rigorous Evaluation: Implement frameworks like LMMs-Eval and define production metrics to continuously monitor model behavior.Scalable Infrastructure & Serving: Master batching, caching, rate-limiting, and cost-management techniques tailored specifically for heavy multimodal workloads.Whether you are building document processing pipelines, visual search engines, or autonomous agent frameworks, Vision-Language Models in Production provides the blueprints, patterns, and source-code insights needed to build resilient, cost-effective, and secure AI systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798184642956
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Vision-Language Models in Production: Architecting Multimodal LLM Applications: From Vision-Language API to Self-Hosted Model (Production AI Engineering Series)
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Paperback. Zustand: new. Paperback. The Engineer's Definitive Guide to Deploying Multimodal AIVision-language models (VLMs) represent the fastest-growing sector of AI deployment. While GPT-4o, Gemini 1.5 Pro, and open-source breakthroughs like LLaVA and InternVL2 have made multimodal AI a reality, transitioning from an API demo to a robust production system remains a monumental challenge. This book bridges that gap, offering a hands-on, engineering-first approach to shipping VLM-powered products.What you will master inside: Architectural Fundamentals: Go deep into visual encoders, projection layers, and language models to optimize prompt engineering and mitigate unexpected failure modes.Model Selection Frameworks: Learn how to weigh proprietary APIs against open-source alternatives based on latency, cost, privacy, and performance benchmarks.Production-Grade Preprocessing: Build highly efficient image pipelines that cut token consumption without sacrificing critical visual intelligence.Multimodal RAG: Design and scale retrieval systems that handle complex corpora containing both structured text and rich visual data.Rigorous Evaluation: Implement frameworks like LMMs-Eval and define production metrics to continuously monitor model behavior.Scalable Infrastructure & Serving: Master batching, caching, rate-limiting, and cost-management techniques tailored specifically for heavy multimodal workloads.Whether you are building document processing pipelines, visual search engines, or autonomous agent frameworks, Vision-Language Models in Production provides the blueprints, patterns, and source-code insights needed to build resilient, cost-effective, and secure AI systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798184642956
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