New Releases, Enhancements, Changes + Arize in the News!
surrogate_explainability=flag to the pandas logger. Using the surrogate explainability approach, users have the option to pass a flag with a request to send data that would produce SHAP values. When the flag is enabled, a tree-based surrogate model is trained using the dataset's features and predictions. The surrogate model then generates SHAP values before sending the combined dataset to the Arize platform.
With surrogate explainability, users can now easily generate feature importance values without having to maintain an extra computation pipeline.
Arize:Observe - All Day Virtual Event, March 29th
Tune into Arize:Observe on March 29th to hear from industry experts like UMAP's Founder Leland McInnes, Coinbase Director of Engineering Chintan Turakhia, Chick-fil-A Senior Lead Machine Learning Engineer Korri Jones, MBA, Uber Director of Engineering Smitha Shyam, and more! Register now!
Rise of the ML Engineer: Flávio Clésio, Artsy
In a wide-ranging interview, ML engineer Flávio Clésio of Artsy stresses the importance of model monitoring and explains why it’s all about “exploit and explore” with recommendation systems. Read more.