Explainable Data Science Workflows
Learn explainable workflows using open source software, semi-automated pipelines, and Python! From data ingestion, cleaning, piping, and modeling, explainability and trust are at the forefront of enterprise data science initiatives. In this talk, learn the best practices for codifying and relaying explainable data science to stakeholders, management, and the end user in a reproducible and portable fashion.
- Best practices for explainable data science
- How to use Lale for semi-automated data science for portability and replicability
- How to utilize explainable algorithms and metrics for data science tasks
Presented by Austin Eovito, Data Scientist, IBM
Austin is a Data Scientist on the Technical Marketing and Evangelism team in San Francisco, California. As a recent graduate student of Florida State University, Austin is focused on the balancing the bleeding-edge research produced by academia and the tools used in applied data science. His Masters thesis was on White Collar Crime using Time-aware Joint-Topic-Sentiment Analysis (TTS). Austin leads IBM's Data Science Masterclass on AI Explainability and currently resides in San Francisco, with his fiance, dog, and two cats.
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