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Workshop: The Key Pillars of ML Observability and How to Apply Them to Your ML Systems

Posted Apr 20
# Explainability and Observability
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SPEAKER
Amber Roberts
Amber Roberts
Amber Roberts
Machine Learning Engineer @ Arize AI

Amber is an Astronomer and Machine Learning Engineer. She comes to Arize from the Splunk ML Product Org where she built out the ML feature solutions as an ML Product Manager. Ambers' current role as a community-oriented Machine Learning Engineer looks to help teams across all industries build ML Observability into their productionalized AI environments.

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Amber is an Astronomer and Machine Learning Engineer. She comes to Arize from the Splunk ML Product Org where she built out the ML feature solutions as an ML Product Manager. Ambers' current role as a community-oriented Machine Learning Engineer looks to help teams across all industries build ML Observability into their productionalized AI environments.

+ Read More
SUMMARY

Taking a model from research to production is hard Ñ and keeping it there is even harder! As more machine learning models are deployed into production, it is imperative to have tools to monitor, troubleshoot, and explain model decisions. Join Amber Roberts, Machine Learning Engineer at Arize AI, in an overview of Arize AIÕs ML Observability platform, enabling ML teams to surface, resolve, and improve model performance issues automatically.

Experience ML observability firsthand with a deep dive into the Arize platform using a practical use case example. Attendees will learn how to identify segments where a model is underperforming, troubleshoot and perform root cause analysis, and proactively monitor your model for future degradations.

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