ISBN: 9798172777097 - production-grade ai architecture: integrating python deep learning systems with a node.js backend: 2 (the full-stack ai systems series) (6 resultados)

Taschenbuch. Condición: Neu. Neuware - How do you turn powerful Python deep learning models into reliable production systems Building an AI model is only half the challenge. The real engineering work begins when that model must serve real users, handle unpredictable traffic, communicate with a web application, and remain secure, observable, and resilient.Production-Grade AI Architecture provides a practical architectural guide to integrating Python deep learning systems with a Node.js backend. It explores the critical boundary between machine learning infrastructure and application engineering, showing how the two ecosystems can work together without creating fragile or difficult-to-maintain systems.Inside, you'll explore: - REST, gRPC, Protobuf, and message queues for reliable AI communication- Node.js API gateways for validation, routing, aggregation, and rate limiting- Redis and BullMQ for asynchronous deep learning workloads- FastAPI and Flask services for exposing Python models to Node.js- Semantic caching and distributed state management to reduce unnecessary computation- Circuit breakers, fallbacks, retries, and graceful degradation for resilient AI systems- mTLS, PII protection, RBAC, and auditing across AI service boundaries- OpenTelemetry, Grafana, and Datadog for full-stack observability- Docker, GitHub Actions, integration testing, and shadow deployments for production deliveryWhether you're designing an AI API, deploying deep learning inference, or building a hybrid Python-Node.js architecture, this book gives you a structured way to think about the systems surrounding your models. Move beyond model prototypes and start engineering AI systems designed for production.…

Paperback. Condición: new. Paperback. How do you turn powerful Python deep learning models into reliable production systems?Building an AI model is only half the challenge. The real engineering work begins when that model must serve real users, handle unpredictable traffic, communicate with a web application, and remain secure, observable, and resilient.Production-Grade AI Architecture provides a practical architectural guide to integrating Python deep learning systems with a Node.js backend. It explores the critical boundary between machine learning infrastructure and application engineering, showing how the two ecosystems can work together without creating fragile or difficult-to-maintain systems.Inside, you'll explore: REST, gRPC, Protobuf, and message queues for reliable AI communicationNode.js API gateways for validation, routing, aggregation, and rate limitingRedis and BullMQ for asynchronous deep learning workloadsFastAPI and Flask services for exposing Python models to Node.jsSemantic caching and distributed state management to reduce unnecessary computationCircuit breakers, fallbacks, retries, and graceful degradation for resilient AI systemsmTLS, PII protection, RBAC, and auditing across AI service boundariesOpenTelemetry, Grafana, and Datadog for full-stack observabilityDocker, GitHub Actions, integration testing, and shadow deployments for production deliveryWhether you're designing an AI API, deploying deep learning inference, or building a hybrid Python-Node.js architecture, this book gives you a structured way to think about the systems surrounding your models. Move beyond model prototypes and start engineering AI systems designed for production. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Condición: new. Paperback. How do you turn powerful Python deep learning models into reliable production systems?Building an AI model is only half the challenge. The real engineering work begins when that model must serve real users, handle unpredictable traffic, communicate with a web application, and remain secure, observable, and resilient.Production-Grade AI Architecture provides a practical architectural guide to integrating Python deep learning systems with a Node.js backend. It explores the critical boundary between machine learning infrastructure and application engineering, showing how the two ecosystems can work together without creating fragile or difficult-to-maintain systems.Inside, you'll explore: REST, gRPC, Protobuf, and message queues for reliable AI communicationNode.js API gateways for validation, routing, aggregation, and rate limitingRedis and BullMQ for asynchronous deep learning workloadsFastAPI and Flask services for exposing Python models to Node.jsSemantic caching and distributed state management to reduce unnecessary computationCircuit breakers, fallbacks, retries, and graceful degradation for resilient AI systemsmTLS, PII protection, RBAC, and auditing across AI service boundariesOpenTelemetry, Grafana, and Datadog for full-stack observabilityDocker, GitHub Actions, integration testing, and shadow deployments for production deliveryWhether you're designing an AI API, deploying deep learning inference, or building a hybrid Python-Node.js architecture, this book gives you a structured way to think about the systems surrounding your models. Move beyond model prototypes and start engineering AI systems designed for production. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…