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Obtain New Insights on Model Behavior with Fiddler

Posted Oct 11, 2022 | Views 628
# Fiddler
# Model Behavior
# Observability
# Fiddler.ai
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Danny Brock
Solutions Engineer @ Fiddler AI

Danny is a senior solutions engineer at Fiddler. When you boil it down, this role is really about evangelizing the value of Fiddler to our prospects and customers through product demonstrations or evaluations with their own models and data. Danny has to understand the specifics around customer challenges to ensure they get maximum value from Fiddler.

Prior to Fiddler, Danny worked for a handful of startups in the analytics space like Endeca, Incorta, and Branchbird which was his own consulting company focused on Hadoop implementations.

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Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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Ben Epstein
Founding Software Engineer @ Galileo

Ben was the machine learning lead for Splice Machine, leading the development of their MLOps platform and Feature Store. He is now a founding software engineer at Galileo (rungalileo.io) focused on building data discovery and data quality tooling for machine learning teams. Ben also works as an adjunct professor at Washington University in St. Louis teaching concepts in cloud computing and big data analytics.

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SUMMARY

Discover how customers are using Fiddler for monitoring, explainability, and model analysis. MLOps Engineers and Data Scientists get hands-on experience on how to instrument ML models with Fiddler for observability. Using a dedicated Fiddler cloud environment on AWS, they register a model and publish model events into the Fiddler Model Performance Management platform.

We use the insights surfaced by Fiddler to better understand data drift, data integrity, and underperforming cohorts. This should be a hot topic for customers driving critical ML initiatives and we hope to see you there.

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