Priya Vattikuti portrait

Meet the Author

PRIYA VATTIKUTI

  • Senior AI/ML Engineer
  • Research Author
  • Technology Speaker
  • Conference Judge
  • Community Mentor

Enterprise AI on AWS

Building scalable AI, machine learning, and Generative AI solutions for the modern enterprise.

Coming Soon

Progress Timeline

  • Writing90%
  • Technical Review60%
  • Editing40%
  • Publishing10%

Expected Release

2027

Why I Am Writing This Book

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.

What You'll Learn

AWS Architecture

Machine Learning

Generative AI & Agentic AI

Data Engineering

Security and Governance

MLOps

Production AI

Real-World Case Studies

Who Should Read This Book?

Software Engineers

Cloud Engineers

AI Engineers

Data Engineers

Students

Enterprise Architects

Sneak Peek

Table of contents

  1. Chapter 1

    Introduction to Enterprise AI

  2. Chapter 2

    IAM Role Lifecycle Management and Identity Governance

  3. Chapter 3

    Zero Trust Architecture for Multi-Account AI and Data Platforms

  4. Chapter 4

    Designing Enterprise AI Solutions

  5. Chapter 5

    Training Machine Learning Models

Showing 5 of 23 chapters.

Download Full Chapter List (PDF)

Why This Book Matters Now

A practical roadmap for building secure, scalable, and production-ready Enterprise AI on AWS.

AWS Architecture

Learn proven AWS architectures for designing secure, scalable, and resilient Enterprise AI platforms.

Generative AI

Build enterprise-grade Generative AI applications using Amazon Bedrock, RAG, AI agents, and modern LLM patterns.

MLOps

Create reliable ML pipelines for training, deployment, monitoring, and continuous improvement.

Security

Implement governance, identity, compliance, and responsible AI practices for enterprise environments.

Enterprise Architecture

Design cloud-native architectures that are scalable, resilient, secure, and optimized for Enterprise AI workloads.

Real-world Projects

Explore practical case studies, reference architectures, and implementation lessons from enterprise AI projects.

Book Journey

  • October 2025

    Started writing

    Completed
  • January 2026

    Research and chapter outlines

    Completed
  • August 2026

    First draft - In progress

    Current
  • November 2026

    Technical review and revisions

    Upcoming
  • February 2027

    Publishing

    Upcoming

Join the Journey

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“Great AI systems aren't built by models alone—they're built through thoughtful architecture, reliable engineering, and continuous learning.”