Jun 15 - Jun 16, 2022
In-Person
Discover strategies to ensure Machine Learning models are accountable & fair to build secure & responsible AI. Topics covered: • Bias • Responsible AI • ML Fairness • Data Governance • Predictive Analytics • Data Efficiency • . . . and more!