Explainable Monitoring for Successful Impact with AI Deployments

2021-03-11

This conversation was part of Fiddler's 3rd annual Explainable AI Summit on October 21, 2020. Training and deploying ML models are relatively fast and cheap, but maintaining, monitoring, and governing them over time is difficult and expensive. An Explainable ML Monitoring system extends traditional monitoring to provide deep model insights with actionable steps. Our panelists discuss ways to increase transparency and actionability across the entire AI lifecycle using explainable monitoring, allowing for a better...

This conversation was part of Fiddler's 3rd annual Explainable AI Summit on October 21, 2020.

Training and deploying ML models are relatively fast and cheap, but maintaining, monitoring, and governing them over time is difficult and expensive. An Explainable ML Monitoring system extends traditional monitoring to provide deep model insights with actionable steps. Our panelists discuss ways to increase transparency and actionability across the entire AI lifecycle using explainable monitoring, allowing for a better understanding of problem drivers, root cause issues, and model analysis through AI deployment.

For more information, please visit http://bit.ly/FiddlerPodcast

Panelists:

Peter Skomoroch, Machine Learning Advisor

Abhishek Gupta, Head of Engineering, Hired, Inc.

Natalia Burina, AI Product Leader, Facebook

Kenny Daniel, Co-Founder and CTO, Algorithmia

Moderated by Rob Harrell, Senior Product Manager, Fiddler

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