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Designing AI in Hybrid and Multi-Cloud: Master Data, Models, and Infrastructure Across Distributed Cloud Platforms
Paperback
Publisher: Independently Published
Published: Dec 6 2025
Pages: 174
Weight: 0.69
Height: 0.37 Width: 7.00 Depth: 10.00
Language: English
Designing AI in Hybrid and Multi-Cloud: Master Data, Models, and Infrastructure Across Distributed Cloud Platforms
How do you build intelligent, high-performance AI systems that span on-prem data centers, private cloud environments, and multiple public cloud providers-without losing control over security, reliability, or cost? Modern organizations face constant pressure to scale AI workloads, meet regulatory demands, and stay ahead of rapid model innovation. Yet traditional cloud strategies and single-provider platforms often break down under real-world constraints. This book provides a practical blueprint for succeeding in a distributed, hybrid, and multi-cloud landscape.
Designing AI in Hybrid and Multi-Cloud delivers a comprehensive framework for developing, scaling, and operating AI across heterogeneous environments. From unified identity and security to distributed data pipelines, vector stores, federated learning, and cross-cloud failover, this book converts complexity into actionable strategy. Every principle, pattern, and architectural choice is backed by real-world implementation guidance-so you set up systems that work under pressure, at enterprise scale.
Readers gain hands-on clarity about:
Building hybrid and multi-cloud AI architecture that balances performance, compliance, and cost
Designing data pipelines, data lakes, feature stores, and vector-based retrieval with consistency across environments
Deploying and governing machine learning models using containerization, registries, automation, and reproducible builds
Enforcing strong identity, security, and policy controls across cloud providers without fragmented access or tooling
Implementing observability, monitoring, incident response, and disaster recovery for distributed AI workloads
Managing spend, resource allocation, and cross-cloud optimization with confidence and transparency
Applying practical decision frameworks to evaluate when multi-cloud or hybrid architecture is truly the right fit
Whether you work in regulated industries, operate large-scale ML platforms, or want to avoid vendor lock-in and future architectural debt, this book shows you what matters most-and how to get it right the first time.
If you're an AI engineer, cloud architect, DevOps/SRE, platform engineer, or technical leader designing next-generation AI infrastructure, this resource helps you move beyond hype and into execution. Instead of theory or single-cloud limits, you get real engineering clarity, production patterns, and the confidence to scale distributed AI across any platform.
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