MLOPs On Highly Sensitive Data

Speakers:
Andreea Munteanu: AI/ML Product Manager
Canonical & Maciej Mazur: Principle ML Engineer, Canonical

MLOps is used in various organizations that operate on very sensitive datasets. For instance, pharmaceutical and life science companies handle human DNA samples, healthcare institutions training models on patient data, or highly regulated environments like telecom and financial companies. End users, machine learning engineers or data scientists can be concerned about cloud-native workloads would expose them more to software vulnerabilities, data leaks, or any lack of data protection measures. In reality, it's just the opposite. This presentation will cover how you can improve compliance and security with features like Kubernetes strict confinement, blockchain-based tokenization and privacy-enhancing technologies like confidential computing. The talk will feature a case study by a life sciences company that created customized treatments using these technology building blocks. After the talk, you will understand how you can apply them yourself on cloud envorinments MLOps using Kubeflow.