Ever wondered how to protect your AI models from being copied without permission? As companies invest millions in AI, safeguarding intellectual property is critical. Join us to explore scalable watermarking techniques for AI models using Kubernetes.

Learn how to embed subtle, detectable digital fingerprints into machine learning models—proving ownership without affecting performance. Discover how Kubernetes can automate these processes within your ML pipeline, addressing key challenges like:

• Designing robust watermarking pipelines for end-to-end model protection.
• Leveraging Kubernetes custom controllers and (Custom Resource Definition) CRDs for automation.
• Monitoring and debugging watermarked models in production.
• Balancing protection strength with inference speed.
• Compliance, auditing, and recovery strategies.

Ideal for platform engineers, ML teams, and architects, this session offers practical solutions for secure, scalable AI model protection.