MLOps Coffee Sessions #110 with David Bayliss, Chief Data Scientist of LexisNexis Risk Solutions, Just Fetch the Data and then... co-hosted by Vishnu Rachakonda.
// Abstract
Composing data to extract features can be a significant problem. Key factors are the data size, compliance restrictions, and real-time data. Ethics (and law) can drive extremely complex audit requirements. In the cloud, you can do anything - at a price.
// Bio
One of the creators of the world's first big data platform (HPCC); David has been tackling big data problems for two decades. A mathematician, compiler writer, and data sponge with more than five dozen patents spanning platforms linking, and search.
Most inventors think outside the box; David can't even remember where the box is. He leads the team that creates their core Data Science methods used by hundreds of data scientists.
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// Related Links
Interesting insight in this post. Would be cool to learn from David about his view on things
https://www.google.com/url?q=https://www.linkedin.com/posts/david-bayliss-426556a_datascience-platform-portability-activity-6913448643303759872-2dqq?utm_source%3Dlinkedin_share%26utm_medium%3Dmember_desktop_web&sa=D&source=calendar&ust=1649078059106132&usg=AOvVaw26wAevExeEfW_AdZSA8UhF
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Timestamps:
[00:00] Introduction to David Bayliss
[01:03] Takeaways
[04:56] LexisNexis and David's role
[07:15] Evolution of LexisNexis in 20 years with so many use cases
[08:51] Role of David in structuring data for working with data change
[14:32] Data management and data access
[17:45] Unique challenges of scale, use case, and diversity at LexisNexis
[24:47] Tardis Iron Box
[30:05] Iron Box translation
[32:56] JVM for data science
[34:24] Iron Box meaning
[36:52] Metadata with PII
[39:08] Detrimental privacy / Hairy Kneecap Theory
[40:57] Speeding things up and Anonymized linking
[46:47] What kept David working at LexisNexis?
[50:30] Wrap up
