Build High Performance MLOps With ML Monitoring and AI Explainability

Explore the critical importance of monitoring in machine learning success through this insightful 35-minute conference talk by Amit Paka, CPO and Co-founder of Fiddler AI. Delve into the key reasons why ML models can silently fail and lose predictive power, including model drift, data integrity issues, outliers, and bias. Discover how cutting-edge Explainable AI and model analytics can quickly identify root causes of operational issues, significantly reducing troubleshooting time. Learn about the iterative nature of MLOps and how model and cohort comparisons can accelerate time to market for new models. Gain valuable insights into building high-performance MLOps systems that leverage ML monitoring and AI explainability to ensure fair, ethical, and responsible AI implementation.

Build High Performance MLOps With ML Monitoring and AI Explainability