Explore how Kubeflow and MLOps can enhance the security of machine learning workloads in this 40-minute conference talk by David Aronchick from Microsoft. Delve into the challenges of transitioning machine learning models from development to production, focusing on potential vulnerabilities and degradation risks. Learn about implementing a robust MLOps process using Kubeflow to address common pitfalls in machine learning workflows. Discover techniques for ensuring reproducibility, validation, versioning, tracking, and safe deployment of ML models. Gain insights into the future direction of MLOps and its potential to accelerate development while mitigating risks. Examine various types of attacks on ML models, including adversarial examples and data poisoning, and explore strategies to defend against them. Understand the importance of building efficient MLOps pipelines to continuously improve model performance and security. Discuss the reality of ML security threats and the necessity of proactive measures in safeguarding your machine learning workloads. Read more