Lilly Tech Systems
Secure ML Pipelines
Build end-to-end secure machine learning workflows with MLSecOps principles. Learn to protect every stage from data ingestion through model deployment with signed models, encrypted inference, access control, and compliance logging.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is MLSecOps? The security landscape for ML pipelines, threat modeling, and the secure ML development lifecycle.
2. Secure Training
Data validation, signed datasets, reproducible builds, isolated training environments, and supply chain integrity.
3. Secure Deployment
Model signing, encrypted inference, secure serving infrastructure, container hardening, and secrets management.
4. Access Control
RBAC for ML systems, API authentication, model registry permissions, and principle of least privilege.
5. Audit Logging
Compliance logging, prediction auditing, data lineage tracking, and regulatory requirements for AI systems.
6. Best Practices
MLSecOps maturity model, security champions program, incident response, and continuous security improvement.
What You'll Learn
By the end of this course, you'll be able to:
Secure Training Pipelines
Protect data integrity, validate training inputs, and ensure reproducible, tamper-resistant model training.
Deploy Models Safely
Sign models, encrypt inference, harden serving infrastructure, and manage secrets securely.
Implement Access Controls
Apply RBAC, manage permissions across ML registries, and enforce least-privilege principles.
Maintain Audit Trails
Build comprehensive logging for compliance, track data lineage, and meet regulatory requirements.