Securing the AI/ML Development Lifecycle: A Practical Guide to Secure AI Engineering
Securing the AI/ML Development Lifecycle: A Practical Guide to Secure AI Engineering
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 9m | 130 MB
Instructor: Ed Moyle
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 9m | 130 MB
Instructor: Ed Moyle
This course explores security throughout the lifecycle of ML/AI systems. Instructor Ed Moyle covers process-aware approaches for “building security in” including AISecOps and MLSecOps. Additionally, Ed explores mechanisms such as AI bills of materials (BOMs) and ways to adapt traditional application/product security efforts to AI-enabled products and software.
Learning objectives
- Analyze security requirements specific to AI/ML systems throughout their development and deployment lifecycle.
- Evaluate traditional security frameworks for adaptation into AI-enabled applications and MLOps/MLSecOps processes.
- Design process-aware security approaches for AI/ML systems including appropriate Bills of Materials (BOMs).
- Create security management strategies for safeguarding AI components during implementation and production.
- Synthesize DevSecOps principles with AI/ML-specific security requirements to develop comprehensive MLSecOps and AISecOps frameworks.
Securing the AI/ML Development Lifecycle: A Practical Guide to Secure AI Engineering
