From Model to Production: A Unified Journey

In this talk, Yuval Fernbach explores the integration of MLOps with traditional software development practices. He discusses how unifying these processes enhances security, streamlines deployments, and allows ML models to become an integral part of the company-wide deployment strategy. The presentation highlights the challenges of ML project failures, the evolution of ML platforms, and the critical need to bridge the gap between DevOps and MLOps silos. It also delves into the significant security risks associated with machine learning models, including potential vulnerabilities from dependencies, custom code, container images, and even training data, emphasizing the importance of managing security throughout the entire ML lifecycle from development to runtime.