How to Design ML Observability for High-Risk AI Use Cases

Learn essential strategies for implementing Machine Learning Observability in high-risk AI applications through this comprehensive one-hour webinar. Explore how MLOps has streamlined baseline processes while addressing the critical gap in ML acceptance protocols. Discover key approaches to managing inherent challenges in AI/ML models, including their lack of explainability, production risks, and complex auditing requirements. Master the implementation of crucial layers such as explainability, monitoring, auditability, data privacy, and risk mitigation to ensure stakeholder acceptance. Delve into fundamental ML Observability concepts, practical applications for model monitoring, explainability techniques, auditing processes, and learn to design effective policy frameworks for managing model usage risks in production environments.