The New Stack Podcast

This Week In Machine Learning & AI Introduces the TWIMLcon Conference

Episode Summary

On this episode of The New Stack Makers, TNS Founder & EiC Alex Williams sits down with Sam Charrington, Founder of This Week in Machine Learning & AI (TWiML & AI). The inaugural TWIMLcon conference takes place October 1st-2nd, 2019 at the Mission Bay Conference Center in San Francisco, California. TWIMLcon aims to bring a fresh perspective to AI & ML events, growing out of conversations that Charrington had with enterprises that, “Tended to be at a very interesting transition point.” Noting that he heard towards the end of last year that, “Companies kicked off a lot of machine learning proof-of-concept types of projects, they had some initial successes, their data science teams were out evangelising, and some of those proof-of-concepts were starting to mature [...] And so all of a sudden these organizations, they were challenged with the transition from ‘How do I successfully execute an individual machine learning project,’ to ‘How do I become an engine for delivering machine learning at my organization?’” said Charrington.

Episode Notes

On this episode of The New Stack Makers, TNS Founder & EiC Alex Williams sits down with Sam Charrington, Founder of This Week in Machine Learning & AI (TWiML & AI). The inaugural TWIMLcon conference takes place October 1st-2nd, 2019 at the Mission Bay Conference Center in San Francisco, California.

TWIMLcon aims to bring a fresh perspective to AI & ML events, growing out of conversations that Charrington had with enterprises that, “Tended to be at a very interesting transition point.” Noting that he heard towards the end of last year that, “Companies kicked off a lot of machine learning proof-of-concept types of projects, they had some initial successes, their data science teams were out evangelising, and some of those proof-of-concepts were starting to mature [...] And so all of a sudden these organizations, they were challenged with the transition from ‘How do I successfully execute an individual machine learning project,’ to ‘How do I become an engine for delivering machine learning at my organization?’” said Charrington.