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SKU/Artículo: AMZ-B0G81F9Y8R

The AI-Native Cloud: Architecting and Optimizing Multi-Cloud Systems for Generative AI, Edge Computing, and FinOps

Format:

Paperback

Hardcover

Kindle

Paperback

Detalles del producto

Disponibilidad:
En stock

Peso con empaque:
0.26 kg

Devolución:
Sí

Condición
Nuevo

Producto de:
Amazon

Viaja desde
USA

Sobre este producto

This book, The AI-Native Cloud: Architecting and Optimizing Multi-Cloud Systems for Generative AI, Edge Computing, and FinOps, is a guide for organizations navigating the convergence of cloud computing, Artificial Intelligence (AI), and business finance. The core premise of the book is that the next generation of cloud architecture must be AI-Native, meaning it is fundamentally designed to support and maximize the value of AI, particularly Generative AI (GenAI). 1. The AI-Native Cloud Architecture The book explores the architectural shift required to support demanding AI workloads. This involves moving beyond traditional cloud designs to create systems that are:Built for AI: Leveraging cloud-native components like microservices, containers (e.g., Kubernetes), and serverless computing, which are well-suited for the rapid scaling and flexible deployment needs of AI models.Multi-Cloud and Hybrid: Recognizing that no single cloud provider is optimal for all AI needs. It discusses strategies for architecting systems that span multiple public clouds (AWS, Azure, GCP) and potentially private/on-premises environments to achieve vendor diversity, best-of-breed services, and resilience.2. Generative AI (GenAI) Integration A significant focus is on how to architect for the unique demands of GenAI:Resource Intensity: GenAI model training and inference require massive, specialized resources, particularly GPUs. The book details how to design infrastructures (like GPU clusters) to efficiently handle these workloads.Deployment and Scale: It covers deploying Large Language Models (LLMs) and other GenAI applications across multi-cloud environments, ensuring low-latency inference, and managing the dynamic scaling required for unpredictable demand.3. Edge Computing and Distributed AI The book addresses the growing importance of Edge Computing—bringing AI processing closer to the data source (e.g., in factories, retail stores, or autonomous vehicles).Low-Latency Performance: It provides blueprints for integrating edge devices with the central cloud to enable real-time, low-latency AI inference, which is critical for many AI applications.Distributed Governance: It discusses the challenges of managing and securing AI models and data across a geographically dispersed, multi-cloud and edge landscape.4. FinOps for AI (Financial Operations) FinOps, the practice of bringing financial accountability to the variable spending model of the cloud, is a crucial part of the "Optimizing" focus. For AI, FinOps takes on new complexity:Cost Visibility and Control: AI workloads, especially GenAI, introduce new, often volatile cost metrics (like cost-per-token or GPU-hour). The book emphasizes strategies for granular cost tracking, tagging resources, and creating a culture of financial awareness among engineering, finance, and product teams.Optimization Strategies: It provides practical guidance on cost-saving techniques, such as:Rightsizing: Ensuring AI workloads run on the most appropriate (and cost-effective) instance types.Automation: Using AI-driven agents to automate resource scheduling (e.g., shutting down unused GPU instances) and optimize resource allocation across different clouds.

AR$19.413

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AR$19.413

49% OFF

AR$9.955

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