Lilly Tech Systems
Container Security for ML
Master the security of containerized machine learning workloads. Learn Docker hardening, Kubernetes security policies, GPU container isolation, image scanning with Trivy and Snyk, runtime protection, and secrets management for ML pipelines.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why container security matters for ML workloads, unique challenges of GPU containers, and the ML container threat landscape.
2. Docker Security
Dockerfile hardening, non-root users, read-only filesystems, secrets management, and GPU passthrough security.
3. Kubernetes Security
Pod security standards, RBAC for ML namespaces, network policies, GPU scheduling security, and service mesh integration.
4. Image Scanning
Vulnerability scanning with Trivy, Snyk, and Grype. Scanning CUDA images, ML framework dependencies, and CI/CD integration.
5. Runtime Security
Falco for ML containers, seccomp profiles, AppArmor policies, GPU memory isolation, and anomaly detection.
6. Best Practices
ML container security checklist, supply chain security, model artifact protection, and production deployment patterns.
What You'll Learn
By the end of this course, you'll be able to:
Harden ML Containers
Build secure Docker images for ML workloads with minimal attack surface, non-root execution, and proper secrets management.
Secure Kubernetes ML
Deploy ML workloads on Kubernetes with proper RBAC, network policies, pod security standards, and GPU scheduling controls.
Scan for Vulnerabilities
Integrate Trivy, Snyk, and other scanning tools into your ML CI/CD pipeline to catch vulnerabilities before deployment.
Monitor Runtime Security
Implement runtime security monitoring with Falco, detect anomalous behavior, and enforce security policies in production.