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Wednesday, October 17 • 11:00am - 11:20am
Monitoring AI with AI

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Environment misconfiguration or upstream data pipeline inconsistency can silently kill the model performance.
Common production incidents include:
- Data drifts, new data, wrong features
- Vulnerability issues, adversarial attacks
- Concept drifts, new concepts, expected model degradation
- Dramatic unexpected drifts
- Biased Training set / training issue
- Performance issue
In this talk we'll discuss a solution, tooling and architecture that allows machine learning engineer to be involved in delivery phase and take ownership over deployment and monitoring of machine learning pipelines.

avatar for Iskandar Sitdikov

Iskandar Sitdikov

ML/Software engineer, Hydrosphere.io
Iskandar Sitdikov is Hydrosphere.io ML engineer with rich practical background both in Machine Lerarning and Big Data fields. His latest tasks lie in the area of research and prototyping data anomalies and concept drifts detection methods in ML production.

Wednesday October 17, 2018 11:00am - 11:20am EDT
Horace Mann