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{ "item_title" : "Vision-Language Models in Production", "item_author" : [" Chatvariety Team "], "item_description" : "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.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/79/818/464/9798184642956_b.jpg", "price_data" : { "retail_price" : "9.99", "online_price" : "9.99", "our_price" : "9.99", "club_price" : "9.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Vision-Language Models in Production|Chatvariety Team

Vision-Language Models in Production : Architecting Multimodal LLM Applications: From Vision-Language API to Self-Hosted Model

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Overview

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.

This item is Non-Returnable

Details

  • ISBN-13: 9798184642956
  • ISBN-10: 9798184642956
  • Publisher: Independently Published
  • Publish Date: June 2026
  • Dimensions: 9 x 6 x 0.18 inches
  • Shipping Weight: 0.28 pounds
  • Page Count: 86

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