Federated ML for Edge Applications with Justin Norman

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

In this episode of our Strata Data conference series, we’re joined by Justin Norman, Director of Research and Data Science Services at Cloudera Fast Forward Labs.

Fast Forward Labs was an Applied AI research firm and consultancy founded by Hilary Mason, who’s TWIML Talk episode remains an all-time fan favorite. My chat with Justin took place on the 1 year anniversary of Fast Forward Labs’ acquisition by Cloudera, so we start with an update on the company before diving into a look at some of their recent and upcoming research projects. Specifically, we discuss their recent report on Multi-Task Learning and their upcoming research into Federated Machine Learning for AI at the edge.

Thanks to our Sponsors!

Thanks to Cloudera and Capital One for their continued support of the podcast and their sponsorship of this series.

Cloudera’s modern platform for machine learning and analytics, optimized for the cloud, lets you build and deploy AI solutions at scale, efficiently and securely, anywhere you want. In addition, Cloudera Fast Forward Lab’s expert guidance helps you realize your AI future, faster. To learn more, visit Cloudera’s Machine Learning resource center at cloudera.com/ml.

At the NIPS Conference in Montreal this December, researchers from Capital One will be co-hosting a workshop focused on Challenges and Opportunities for AI in Financial Services and the Impact of Fairness, Explainability, Accuracy, and Privacy. A call for papers is open now through October 25, for more information or submissions, visit twimlai.com/c1nips. A limited number of full NIPS Conference tickets are also available for accepted speakers (one full-price ticket only, applied directly to the accepted speaker),and will be made available along with author notifications on Oct 29, 2018.

About Justin

Mentioned in the Interview

“More On That Later” by Lee Rosevere licensed under CC By 4.0

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