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Synopsis
Your AI model may be powerful—but without the right backend, it cannot become a reliable production system.
Architecting the AI-Driven Node.js Backend is a practical guide for developers, software architects, and AI engineers who want to build scalable infrastructure for deep learning applications using Node.js.
Instead of treating AI as a standalone model, this book focuses on the backend architecture required to connect models with APIs, data pipelines, databases, asynchronous workflows, security systems, and production infrastructure.
Inside, you'll learn how to:
- Design REST, GraphQL, and gRPC APIs for machine learning inference
- Build asynchronous AI workflows with Redis, BullMQ, and distributed workers
- Process high-volume data streams and optimize preprocessing with Node.js
- Connect Python-based AI models to Node.js through APIs, IPC, child processes, and WebAssembly
- Run inference directly with ONNX Runtime
- Scale AI backends using load balancing, auto-scaling, rate limiting, and stateless services
- Store vector embeddings, AI metadata, training data, and application state effectively
- Secure machine learning APIs with authentication, access control, encryption, and auditing
- Test and debug deep learning pipelines, memory leaks, and asynchronous bottlenecks
- Containerize and deploy Node.js AI services with Docker, CI/CD, and zero-downtime strategies
Build the backend your AI deserves—and take your models from isolated experiments to scalable production services.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Architecting the AI-Driven Node.js Backend (Paperback)
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Paperback. Etat : new. Paperback. Your AI model may be powerful-but without the right backend, it cannot become a reliable production system.Architecting the AI-Driven Node.js Backend is a practical guide for developers, software architects, and AI engineers who want to build scalable infrastructure for deep learning applications using Node.js.Instead of treating AI as a standalone model, this book focuses on the backend architecture required to connect models with APIs, data pipelines, databases, asynchronous workflows, security systems, and production infrastructure.Inside, you'll learn how to: Design REST, GraphQL, and gRPC APIs for machine learning inferenceBuild asynchronous AI workflows with Redis, BullMQ, and distributed workersProcess high-volume data streams and optimize preprocessing with Node.jsConnect Python-based AI models to Node.js through APIs, IPC, child processes, and WebAssemblyRun inference directly with ONNX RuntimeScale AI backends using load balancing, auto-scaling, rate limiting, and stateless servicesStore vector embeddings, AI metadata, training data, and application state effectivelySecure machine learning APIs with authentication, access control, encryption, and auditingTest and debug deep learning pipelines, memory leaks, and asynchronous bottlenecksContainerize and deploy Node.js AI services with Docker, CI/CD, and zero-downtime strategiesWhether you're building an AI-powered API, intelligent application, model-serving platform, or enterprise backend, this book connects Node.js backend engineering with practical AI infrastructure. Build the backend your AI deserves-and take your models from isolated experiments to scalable production services. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798171850012
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Architecting the AI-Driven Node.js Backend: Building Scalable Infrastructure for Deep Learning Applications (The AI Backend Architecture Series)
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Etat : New. Print on Demand. N° de réf. du vendeur I-9798171850012
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Architecting the AI-Driven Node.js Backend (Paperback)
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Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. Your AI model may be powerful-but without the right backend, it cannot become a reliable production system.Architecting the AI-Driven Node.js Backend is a practical guide for developers, software architects, and AI engineers who want to build scalable infrastructure for deep learning applications using Node.js.Instead of treating AI as a standalone model, this book focuses on the backend architecture required to connect models with APIs, data pipelines, databases, asynchronous workflows, security systems, and production infrastructure.Inside, you'll learn how to: Design REST, GraphQL, and gRPC APIs for machine learning inferenceBuild asynchronous AI workflows with Redis, BullMQ, and distributed workersProcess high-volume data streams and optimize preprocessing with Node.jsConnect Python-based AI models to Node.js through APIs, IPC, child processes, and WebAssemblyRun inference directly with ONNX RuntimeScale AI backends using load balancing, auto-scaling, rate limiting, and stateless servicesStore vector embeddings, AI metadata, training data, and application state effectivelySecure machine learning APIs with authentication, access control, encryption, and auditingTest and debug deep learning pipelines, memory leaks, and asynchronous bottlenecksContainerize and deploy Node.js AI services with Docker, CI/CD, and zero-downtime strategiesWhether you're building an AI-powered API, intelligent application, model-serving platform, or enterprise backend, this book connects Node.js backend engineering with practical AI infrastructure. Build the backend your AI deserves-and take your models from isolated experiments to scalable production services. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798171850012
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Architecting the AI-Driven Node.js Backend : Building Scalable Infrastructure for Deep Learning Applications
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. Neuware - Your AI model may be powerful-but without the right backend, it cannot become a reliable production system.Architecting the AI-Driven Node.js Backend is a practical guide for developers, software architects, and AI engineers who want to build scalable infrastructure for deep learning applications using Node.js.Instead of treating AI as a standalone model, this book focuses on the backend architecture required to connect models with APIs, data pipelines, databases, asynchronous workflows, security systems, and production infrastructure.Inside, you'll learn how to: - Design REST, GraphQL, and gRPC APIs for machine learning inference- Build asynchronous AI workflows with Redis, BullMQ, and distributed workers- Process high-volume data streams and optimize preprocessing with Node.js- Connect Python-based AI models to Node.js through APIs, IPC, child processes, and WebAssembly- Run inference directly with ONNX Runtime- Scale AI backends using load balancing, auto-scaling, rate limiting, and stateless services- Store vector embeddings, AI metadata, training data, and application state effectively- Secure machine learning APIs with authentication, access control, encryption, and auditing- Test and debug deep learning pipelines, memory leaks, and asynchronous bottlenecks- Containerize and deploy Node.js AI services with Docker, CI/CD, and zero-downtime strategiesWhether you're building an AI-powered API, intelligent application, model-serving platform, or enterprise backend, this book connects Node.js backend engineering with practical AI infrastructure. Build the backend your AI deserves-and take your models from isolated experiments to scalable production services. N° de réf. du vendeur 9798171850012
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Expédition à EUR 35
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