Secure Python ML - The Major Security Flaws in the ML Lifecycle and How to Avoid Them

Explore the critical security risks in the machine learning lifecycle and learn how to mitigate them in this 31-minute conference talk from EuroPython 2022. Uncover common security flaws across each phase of the ML process, from feature engineering to model deployment and monitoring. Follow along with a hands-on example of training, packaging, and deploying a model from scratch, identifying key risk areas and learning about tools and practices to address them. Gain a robust understanding of security best practices in MLOps, equipping yourself with the knowledge to advocate for and implement MLSecOps in production environments. Discover frameworks and approaches to secure ML models, pipelines, and services, ensuring the safety and reliability of your machine learning systems in critical use cases across industries.