ML Observability, A Critical Piece For Making Models Work In the Real World

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Topic: Lunch & Learn: ML Observability, A Critical Piece For Making Models Work In the Real World

Speaker: Amber Roberts, Sales Engineer at Arize
Amber Roberts is an astrophysicist and machine learning engineer, who was previously the Head of AI at Insight Data Science. Since then she has been at Splunk in their ML Product Org to build out ML feature solutions as a ML Product Manager. She now joins us at Arize as a ML Sales Engineer looking to help teams across industries build ML Observability into their productionalized AI environments.

Abstract:
According to a recent survey, 84.3% of data scientists and ML engineers say the time it takes to detect and diagnose problems with a model is an issue for their teams.
How can teams quickly visualize where and why problems and resolve issues with models in production faster? Machine learning observability is a key part of the answer – and a critical element for any modern ML infrastructure stack. Arize AI, an early pioneer and leader in ML observability, tracks billions of ML predictions on behalf of enterprises and disruptive startups.
In this session, speaker will cover the essentials of ramping up an ML observability practice. In this lunch and learn session, we’ll cover:
- Common challenges in productionalizing ML
- Drift analysis
- Performance analysis
- Data quality checks
- Explainability

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