Training and deploying ML models is relatively fast and cheap, but maintaining, monitoring, governing, and ensuring responsible use of them over time is difficult and expensive. An Explainable ML Monitoring system extends traditional monitoring to provide deep model insights with actionable steps. We will discuss ways to increase transparency and actionability across the entire AI lifecycle using explainable monitoring, allowing for better understanding of problem drivers, root cause issues, and model analysis through AI deployment. As businesses, consumers, and regulators are calling for more transparency and accountability in AI solutions, we will discuss how a combination of explainability and monitoring provide more trustworthy, transparent, and accountable AI at every stage of the AI lifecycle.
