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, stake-holders, and other data scientists. From data ingestion, cleaning and feature selection, and ultimately model selection, explainability can be incorporated into a data scientist's workflow.
Using a combination of semi-automated and open source software, this talk walks you through an explainable workflow.
Please my on demand webcast, Explainable Workflows using Python: DSE Presents Chat with the Lab here.
Reply with any of your questions!
Thanks,
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Austin Eovito
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