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Our guest today is Wojtek Kuberski, Co-Founder and CTO at NannyML.

In our conversation, we first discuss Wojtek's experience working as a freelancer. We then talk about NannyML: the platform for post deployment Data Science. We dive deep into model monitoring and discuss the key causes of model failure including covariate shift, concept drift and bad data quality. Wojtek also explains how NannyML's algorithms can estimate model performance without access to labels.

If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

Check out NannyML: https://www.nannyml.com/

Follow Wojtek on LinkedIn: https://www.linkedin.com/in/wojtek-kuberski/

Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/

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(00:00) Intro

(02:36) How Wojtek got into AI

(04:48) Early Projects & Learnings in Freelance

(13:40) Building NannyML

(16:20) Model Monitoring

(18:32) Covariate Shift vs Concept Drift

(24:57) Technical Insights into Model Monitoring

(27:56) NannyML’s Platform & Workflow

(35:50) Retraining Model & Model Failures

(41:39) Future of NannyML

(47:09) Career Advice