OpenVINO Inference and Deployment Techniques: The Complete Guide for Developers and Engineers
William Smith
Aug 2025 · HiTeX Press
Ebook
250
Pages
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About this ebook
"OpenVINO Inference and Deployment Techniques"
OpenVINO Inference and Deployment Techniques is an authoritative guide for engineers, researchers, and practitioners seeking to optimize and deploy deep learning models efficiently across diverse hardware platforms. Beginning with a comprehensive analysis of the OpenVINO toolkit’s architecture, supported frameworks, and model optimization pipeline—including conversion, quantization, and compression—the book seamlessly bridges theoretical foundations with practical know-how. Readers will find detailed explorations of inference engine internals, device plugin integration across CPUs, GPUs, VPUs, and FPGAs, and essential strategies for custom operation extension and model optimizer customization.
This volume moves beyond the basics, empowering readers to implement robust real-world solutions for edge, embedded, and cloud environments. With dedicated chapters on asynchronous and synchronous inference pipelines, resource management, heterogeneous execution, and fault-tolerant deployment, it prepares practitioners to overcome the inherent challenges of deploying scalable AI services. The text also details best practices for containerization, orchestration with Kubernetes, dynamic hybrid inference, and over-the-air model updates, ensuring seamless operations from edge devices to cloud infrastructures.
Recognizing the criticality of security, governance, and observability, the book delves into securing inference pipelines, maintaining model integrity, achieving compliance, and integrating with modern telemetry and monitoring stacks. Readers will discover techniques for continuous diagnostics, profiling, A/B testing, and canary deployments, alongside emerging trends such as federated inference, explainability, and advanced MLOps integration. By weaving foundational concepts with future-forward patterns, OpenVINO Inference and Deployment Techniques equips readers with a holistic, production-ready toolkit for deployment and innovation in AI-driven applications.
OpenVINO Inference and Deployment Techniques is an authoritative guide for engineers, researchers, and practitioners seeking to optimize and deploy deep learning models efficiently across diverse hardware platforms. Beginning with a comprehensive analysis of the OpenVINO toolkit’s architecture, supported frameworks, and model optimization pipeline—including conversion, quantization, and compression—the book seamlessly bridges theoretical foundations with practical know-how. Readers will find detailed explorations of inference engine internals, device plugin integration across CPUs, GPUs, VPUs, and FPGAs, and essential strategies for custom operation extension and model optimizer customization.
This volume moves beyond the basics, empowering readers to implement robust real-world solutions for edge, embedded, and cloud environments. With dedicated chapters on asynchronous and synchronous inference pipelines, resource management, heterogeneous execution, and fault-tolerant deployment, it prepares practitioners to overcome the inherent challenges of deploying scalable AI services. The text also details best practices for containerization, orchestration with Kubernetes, dynamic hybrid inference, and over-the-air model updates, ensuring seamless operations from edge devices to cloud infrastructures.
Recognizing the criticality of security, governance, and observability, the book delves into securing inference pipelines, maintaining model integrity, achieving compliance, and integrating with modern telemetry and monitoring stacks. Readers will discover techniques for continuous diagnostics, profiling, A/B testing, and canary deployments, alongside emerging trends such as federated inference, explainability, and advanced MLOps integration. By weaving foundational concepts with future-forward patterns, OpenVINO Inference and Deployment Techniques equips readers with a holistic, production-ready toolkit for deployment and innovation in AI-driven applications.
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