Reseña del libro "AI-Native Platform Engineering (en Inglés)"
AI systems are no longer isolated models behind application APIs. Production AI now depends on a shared platform layer that must coordinate heterogeneous compute, model serving, agents, retrieval, identity, policy, observability, reliability, security, governance, and cost.AI-Native Platform Engineering develops a rigorous architecture for that platform layer. Written for experienced engineers, architects, SREs, platform engineers, security practitioners, and technical leaders, the book treats AI infrastructure as an engineering discipline rather than a catalog of tools. It explains how to build stable platform contracts while models, runtimes, protocols, providers, and accelerators continue to change.Across thirty-one chapters, the book examines workload taxonomy, Kubernetes and accelerator scheduling, inference platforms, durable agent runtimes, internal developer platforms, AI gateways, MCP and A2A interoperability, context and memory services, workload and agent identity, policy as code, AI/ML supply-chain security, regulatory evidence, observability, SLOs, AI FinOps, resilience, multi-tenancy, release engineering, capacity qualification, data movement, lifecycle management, failure injection, training platforms, adversarial validation, and reference architectures.The result is a practical framework for designing AI platforms that remain reliable, governable, secure, economically measurable, and replaceable as the ecosystem evolves. Read more