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Learn how to build and implement secure machine learning pipelines in this 37-minute conference talk from All Things Open. Explore the challenges and solutions for creating robust ML infrastructure that prioritizes security throughout the development and deployment process. Discover practical approaches to protecting data, models, and computational resources while maintaining pipeline efficiency and scalability. Gain insights into best practices for securing ML workflows, including data validation, model versioning, access controls, and monitoring systems. Understand how to address common security vulnerabilities in ML pipelines and implement defensive measures against potential threats. The presentation covers real-world examples and demonstrates techniques for building ML systems that balance performance requirements with security considerations, making it valuable for data scientists, ML engineers, and security professionals working with machine learning infrastructure.
