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Isabel Zimmerman – Explaining model explainability
Isabel Zimmerman - Practical MLOps for better models | PyData Global 2022
Model Explainability Forum
Isabel Zimmerman | Demystifying MLOps | Posit (2022)
Stanford Seminar - ML Explainability Part 1 I Overview and Motivation for Explainability
Isabel Zimmerman Data Science- Quantitative Economics class of 2021
MLOps with vetiver in Python and R | Led by Julia Silge & Isabel Zimmerman
XLoKR 2020 - Invited Talk by Tim Miller: Explainable AI: beware the inmates running the asylum
Beyond Inference: Bringing ML into Production - DevConf.CZ 2021
Delivering ML models with Open Data Hub
Daniel Malinsky: Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning
Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences
