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Data Governance for Data Science with Adam Wood - #578

Data Governance for Data Science with Adam Wood - #578

FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)


Data Governance for Data Science with Adam Wood - #578

FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

ratings:
Length:
40 minutes
Released:
Jun 13, 2022
Format:
Podcast episode

Description

Today we’re joined by Adam Wood, Director of Data Governance and Data Quality at Mastercard. In our conversation with Adam, we explore the challenges that come along with data governance at a global scale, including dealing with regional regulations like GDPR and federating records at scale. We discuss the role of feature stores in keeping track of data lineage and how Adam and his team have dealt with the challenges of metadata management, how large organizations like Mastercard are dealing with enabling feature reuse, and the steps they take to alleviate bias, especially in scenarios like acquisitions. Finally, we explore data quality for data science and why Adam sees it as an encouraging area of growth within the company, as well as the investments they’ve made in tooling around data management, catalog, feature management, and more.
The complete show notes for this episode can be found at twimlai.com/go/578
Released:
Jun 13, 2022
Format:
Podcast episode

Titles in the series (100)

This Week in Machine Learning & AI is the most popular podcast of its kind. TWiML & AI caters to a highly-targeted audience of machine learning & AI enthusiasts. They are data scientists, developers, founders, CTOs, engineers, architects, IT & product leaders, as well as tech-savvy business leaders. These creators, builders, makers and influencers value TWiML as an authentic, trusted and insightful guide to all that’s interesting and important in the world of machine learning and AI. Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning and more.