How MLOps Tools Will Need to Adapt to Responsible and Ethical AI - Stay Ahead of the Curve

Explore the future of MLOps tools and their adaptation to responsible and ethical AI in this 39-minute conference talk from MLOps World: Machine Learning in Production. Dive into the ethical guardrails every MLOps solution should implement to prepare for upcoming regulatory requirements and GDPR compliance. Learn about the 7 key principles of GDPR, core components of compliance, and the definition of personal data according to the regulation. Discover what's currently missing in MLOps systems and examine existing solutions. Understand the 4 pillars of privacy-preserving AI, including privacy concerns in training data, differential privacy, disclosure risk vs. data utility, redaction, de-identification, and synthetic data. Explore the potential of federated learning and gain insights into the EU Draft AI Regulation. Stay ahead of the curve in responsible AI implementation with guidance from Patricia Thaine, Co-Founder & CEO of Private AI and Computer Science PhD Candidate at the University of Toronto.

Intro
7 Key Principles of the GDPR
Some Core Components of GDPR Compliance
Personal Data According to the GDPR
What's Missing Most in MLOps Systems?
What's Out There?
4 Pillars of Privacy-Preserving Al
Privacy Concerns
On Training Data Privacy
Differential Privacy
Disclosure Risk vs. Data Utility
Redaction or De-Id
Synthetic Data
What About Federated Learning?
EU Draft Al Regulation