Explainable AI Workflows using Python

This talk approaches the typical data science workflow with a focus on explainability. Simply put, it focuses on skills and tactics used to help data scientists articulate their findings to end-users, stakeholders, and other data scientists. From data ingestion, cleaning and feature selection, and ultimately model selection, explainability can be incorporated into a data scientists workflow. Using a combination of semi-automated and open source software, this talk walks you through an explainable workflow.
Austin Eovito

Austin is a Data Scientist in IBM, who focuses on the balance of bleeding-edge research produced by academia and the tools used in applied data science.
  • Date: Aug 10, 10:00 AM PST
  • Fee: Free
  • Available Seats: 0 (max 200)
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