Episode 25 — MLOps & Serving Security

This episode introduces MLOps and serving security, focusing on practices that protect the deployment, operation, and continuous delivery of AI models. MLOps extends DevOps principles to AI, requiring controls for model registries, CI/CD pipelines, and serving infrastructure. For certification purposes, learners must know definitions such as model registry, rollback, and shadow deployment, and understand how these components can be secured against tampering or misuse. The exam relevance lies in recognizing how insecure pipelines or serving endpoints become attractive attack surfaces in real-world AI deployments.

Applied perspectives highlight scenarios where compromised registries introduce poisoned models, misconfigured CI/CD pipelines bypass validation, or serving endpoints are targeted with adversarial inputs. Defensive measures include artifact signing, validation gates in CI/CD, monitoring of deployed models, and strict access control for serving APIs. Best practices emphasize reproducibility, rollback mechanisms, and segregation of environments to minimize blast radius in case of compromise. Troubleshooting considerations highlight the risk of shadow deployments introducing vulnerabilities if not audited carefully. For both exam performance and professional practice, learners must be prepared to explain why secure MLOps is the foundation of reliable AI operations. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.