Building Secure and Scalable ML-Driven Cloud Systems: A Practical Engineering Guide

Abstract

Building Secure and Scalable ML-Driven Cloud Systems: A Practical Engineering Guide

Objectives:

1. To develop a comprehensive understanding of designing, deploying, and managing machine
learning systems in cloud environments with a focus on scalability, security, and reliability.
2. To provide practical frameworks for implementing end-to-end MLOps pipelines, including
data engineering, model training, deployment, monitoring, and continuous improvement.
3. To explore advanced techniques for optimizing machine learning systems, such as
auto-scaling, model compression, caching strategies, and leveraging specialized hardware
for high-performance computing.
4. To address critical challenges in ML systems, including security threats, governance, ethical
considerations, and compliance with data privacy regulations in real-world applications.
5. To equip engineers, researchers, and practitioners with the skills and strategies needed to
build production-ready, resilient, and cost-efficient ML-driven systems aligned with
business objectives.

Table of Contents
CHAPTER 1 Foundations of MLOps in the Cloud
CHAPTER 2 Architecting ML Systems for the Cloud
CHAPTER 3 Secure and Scalable Data Management
CHAPTER 4 Large-Scale Data Processing and Feature Engineering
CHAPTER 5 Scalable Model Training and Experimentation
CHAPTER 6 Model Management and Versioning
CHAPTER 7 Infrastructure as Code and Containerization for MLOps
CHAPTER 8 Deploying Models to Production
CHAPTER 9 Monitoring, Observability, and Explainability
CHAPTER 10 A Deep Dive into ML System Security
CHAPTER 11 Advanced Scaling and Optimization Techniques

CHAPTER 12 Governance, Ethics, and MLOps Culture