|May 28, 2020
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Improved Machine Learning with Kedro & MLflow
Hosted by Elliott J. R. Church
Presenter: Michael Bloem, PhD, will explain and demonstrate the Kedro & MLflow machine-learning frameworks.
Kedro and MLflow are relatively new open source software packages that can make the development & deployment of machine learning models (and the data pipelines that support them) more robust, reproducible, & collaborative. In this talk, I will introduce these software packages and describe how they can provide complementary functionality. I will then demonstrate their use from soup to nuts (e.g., from a CSV or database to an API model deployment). Time permitting, I will discuss how pipelines in Python’s scikit-learn package can provide additional complementary functionality that facilitates model deployment.
Michael Bloem is a principal data scientist with Mosaic Data Science. In this role, he leads and executes the design, development, and deployment of data science-enabled solutions with organizations in a variety of verticals. Prior to joining Mosaic Data Science, he led the development of analytics that enabled new Internet-of-Things-based products and services at Steelcase, Inc. and researched air traffic management in the Systems Modeling and Optimization branch of the Aviation Systems Division at NASA Ames Research Center. He received his B.S.E. degree with majors in electrical and computer engineering and economics from Calvin College in 2004 and his M.S. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2007. In 2015, Michael received his PhD in operations research from the Department of Management Science and Engineering at Stanford University.