This module builds on the infrastructure layer by addressing the unique risks found in machine learning build and deployment workflows. Continuous integration and continuous deployment (CI/CD) pipelines accelerate innovation, but they also introduce opportunities for adversaries to slip in malicious dependencies, poisoned data, or corrupted artifacts. Learners will study the anatomy of ML supply chain attacks and discover practical strategies to counter them, such as dependency scanning, code signing, and reproducible builds. The combination of theory, real-world case studies, and a hands-on demo will help learners see how insecure workflows can compromise entire AI systems. By the end of this module, participants will be able to design and implement CI/CD pipelines that embed security into every stage of model development and deployment.

This module brings together infrastructure and workflow security into a forward-looking focus on resilience. No pipeline is immune to compromise or error, but resilient pipelines are designed to detect issues quickly, recover gracefully, and maintain trustworthiness under stress. Learners will study common compromise vectors in ML systems, from adversarial inputs to model drift, and then explore resilience strategies like rollback, redundancy, and drift monitoring. The demo illustrates how even a simple rollback can protect business continuity when a model misbehaves in production. The scenario-based dialogue challenges learners to think critically about balancing speed, reliability, and safety in real-world ML operations. By the end of this module, learners will understand how to engineer resilience into ML pipelines so that failures and attacks become manageable events rather than catastrophic disruptions.