Progress Timeline
Expected Release
2027

Meet the Author
PRIYA VATTIKUTI
Building scalable AI, machine learning, and Generative AI solutions for the modern enterprise.
Coming SoonExpected Release
2027
The real challenge is not building an AI demo. It is taking that demo into a secure, governed, and reliable production system that creates measurable business value. After years of building enterprise AI and cloud-native solutions on AWS, I wrote this book to bridge the gap between experimentation and production with practical patterns, hard-earned lessons, and real deployment guidance.
Software Engineers
Cloud Engineers
AI Engineers
Data Engineers
Students
Enterprise Architects
Table of contents
Introduction to Enterprise AI
IAM Role Lifecycle Management and Identity Governance
Zero Trust Architecture for Multi-Account AI and Data Platforms
Designing Enterprise AI Solutions
Training Machine Learning Models
Showing 5 of 23 chapters.
Download Full Chapter List (PDF)A practical roadmap for building secure, scalable, and production-ready Enterprise AI on AWS.
Learn proven AWS architectures for designing secure, scalable, and resilient Enterprise AI platforms.
Build enterprise-grade Generative AI applications using Amazon Bedrock, RAG, AI agents, and modern LLM patterns.
Create reliable ML pipelines for training, deployment, monitoring, and continuous improvement.
Implement governance, identity, compliance, and responsible AI practices for enterprise environments.
Design cloud-native architectures that are scalable, resilient, secure, and optimized for Enterprise AI workloads.
Explore practical case studies, reference architectures, and implementation lessons from enterprise AI projects.
October 2025
Started writing
January 2026
Research and chapter outlines
August 2026
First draft - In progress
November 2026
Technical review and revisions
February 2027
Publishing