Vision-Language AI(Edition1): Building real-world multimodal applications from theory to production (English Edition) - Tapa blanda

Sinopsis

While most resources treat vision and language as separate domains, the future of AI demands their seamless integration. Vision Language Models (VLMs) represent the frontier of AI, revolutionizing industries from healthcare to autonomous systems, but implementing them remains a complex challenge. This includes integration challenges, deployment complexity, and real-world performance issues. This book helps you move from theoretical AI concepts to production-ready systems.

This book takes you from foundational concepts to deployed, scalable VLM systems through real-world projects across five industries. You will explore the complete development lifecycle from data preprocessing and model architecture to optimization techniques and production deployment strategies. Designed around practical application, every chapter provides battle-tested solutions, complete code implementations, and adaptable architectural patterns that work in live environments. The book will help you build the multimodal AI applications that will define the next decade of technology.

By the end of this book, you will possess the practical skills and architectural expertise needed to build, scale, and manage production-ready Vision Language Models safely and effectively across any domain.

WHAT YOU WILL LEARN

● Build, fine-tune, and deploy VLMs from scratch.

● Master multimodal training, prompt engineering, and optimization.

● Deploy on cloud and edge with monitoring and security.

● Apply VLMs across healthcare, retail, and manufacturing.

● Design agentic AI systems that reason and act autonomously.

WHO THIS BOOK IS FOR

This book is for current and aspiring AI/ML engineers, computer vision specialists, data scientists, technical product managers, and software architects with a basic foundation in Python programming, fundamental machine learning concepts, and deep learning principles using PyTorch.

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