Generative AI Infrastructure-Scaling and Performance Optimization

Generative AI Infrastructure: Scaling and Performance Optimization" is an in-depth exploration of the technical foundations needed to deploy and scale generative AI models efficiently. The book covers the essential components of AI infrastructure, from choosing the right hardware and cloud platforms to optimizing training and inference workloads for performance. Readers will learn about distributed training techniques, GPU/TPU utilization, model compression, and techniques for reducing latency in real-time applications.

The book addresses key challenges in scaling AI systems, including managing large datasets, ensuring data security, and balancing cost-efficiency with performance optimization. Advanced topics like fine-tuning models for specific use cases, automating deployment pipelines, and monitoring performance in production environments are also covered. Designed for data scientists, machine learning engineers, and infrastructure architects, this book combines theoretical knowledge with practical solutions and case studies, helping teams effectively deploy generative AI systems at scale.