Deploying & Scaling AI-Integrated Apps Testing, Cloud Deployment, and Performance Optimization

Building AI-powered apps is one challenge, deploying, scaling, and securing them in production is another. This book is your complete guide to taking AI projects from the lab to the real world with confidence.
In Deploying & Scaling AI-Integrated Apps, readers are guided through the critical foundations of production-ready AI systems. You'll learn not just how to code, but how to design cloud-native, scalable, and cost-efficient infrastructures that can handle real-world AI workloads.
📘 What You'll Learn Inside:Testing AI Apps: Unit, integration, and load testing tailored for non-deterministic AI models.Cloud Infrastructure: How to leverage AWS, GCP, and Azure for compute, storage, and AI-specific services.CI/CD Pipelines: Automating builds, tests, and deployments for faster iteration.Containerization & Orchestration: Using Docker & Kubernetes to package and scale AI workloads.Scaling AI Services: Caching, load balancing, real-time streaming, and cost optimization.Security & Compliance: Protecting data, APIs, and ensuring GDPR/HIPAA/CCPA readiness.Monitoring & Observability: Tracking performance, detecting model drift, and setting up alerts.Edge & Hybrid Deployments: Running AI at the edge for privacy, latency, and offline support.Disaster Recovery & Continuity: Backup, failover, and multi-region deployment strategies.Case Studies: Real-world architectures like e-commerce AI apps and a voice-enabled virtual assistant on AWS.✅ Packed with hands-on code examples, architectural diagrams, and checklists, this book bridges the gap between AI development and enterprise-grade deployment.
Whether you're a developer, architect, or startup founder, this is your go-to resource for building scalable, secure, and production-ready AI applications.

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