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Data Observability for Machine Learning Teams

Posted Apr 12
# Data engineering
# Explainability and Observability
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SPEAKER
Kyle Kirwan
Kyle Kirwan
Kyle Kirwan
CEO & Co-founder @ Bigeye

Kyle Kirwan is the cofounder and CEO of Bigeye, a data observability platform. Before starting Bigeye, he was a Data Product Manager at Uber where he led the development of internal data operations tools that enabled data discovery, lineage, freshness, observability, and incident management for hundreds of data engineers, analysts, and scientists within the company. He lives in New York City with his fiancŽ and prefers Cherry MX Blue keyboard switches.

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Kyle Kirwan is the cofounder and CEO of Bigeye, a data observability platform. Before starting Bigeye, he was a Data Product Manager at Uber where he led the development of internal data operations tools that enabled data discovery, lineage, freshness, observability, and incident management for hundreds of data engineers, analysts, and scientists within the company. He lives in New York City with his fiancŽ and prefers Cherry MX Blue keyboard switches.

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SUMMARY

Once models go to production, observability becomes key to ensuring reliable performance over time. But whatÕs the difference between ÒML ObservabilityÓ and ÒData ObservabilityÓ, and how can ML Engineering teams apply them to maintain model performance? Get fast, practical answers in this lightning talk by UberÕs former leader of data operations tooling, and founder of data observability company, Bigeye.

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