Cloud-Native AI Infrastructure: Kubernetes Orchestration for Distributed Deep Learning and Node.js Backends - Tapa blanda

Sinopsis

AI workloads are no longer confined to a single server or development environment.

Building them for production means coordinating containers, GPUs, data pipelines, APIs, and distributed services without sacrificing reliability.
Cloud-Native AI Infrastructure: Kubernetes Orchestration for Distributed Deep Learning and Node.js Backends explores how to design and operate cloud-native infrastructure for modern AI applications using Kubernetes, Node.js, microservices, and distributed deep learning architectures.
Rather than treating the AI model as an isolated component, this book examines the complete infrastructure surrounding it—from API gateways and asynchronous workflows to model-serving services, event-driven pipelines, security, observability, and scaling.
Inside, you'll explore:

• Designing resilient Node.js AI backends and microservices
• Deploying deep learning models as Kubernetes services
• Connecting Node.js applications with Python-based inference workloads through gRPC
• Building event-driven AI workflows with Kafka, RabbitMQ, and NATS
• Managing distributed preprocessing, caching, and high-volume data flows
• Supporting multi-model deployments, A/B testing, and shadow inference
• Extending Kubernetes to edge environments with K3s
• Securing AI workloads with zero-trust architecture, mTLS, and network policies
• Tracing requests from Node.js gateways through GPU inference
• Monitoring and scaling distributed AI infrastructure

For software engineers, AI engineers, platform engineers, and architects working with Kubernetes, Node.js, machine learning, and cloud-native AI, this book provides an architectural perspective on connecting these technologies into a cohesive production environment.
Build AI infrastructure that can scale beyond the prototype.

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