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Designing an ML Minded Product and a Product Minded ML System with Grace Huang
Creating a real-world machine learning product requires considerations beyond implementation of the machine learning algorithms itself. For example, very often the production environment is a constantly shifting data landscape. A well tested, carefully constructed model can become stale in a matter of days or even hours when data distribution drifts over time. In addition, a sustainable machine learning system needs to run on a healthy data ecosystem where bias is removed or accounted for as much as possible. Finally, evaluating, A/B testing, and launching machine learning product requires considerations very different from conventional product features.
In this webinar, we will share a few lessons learned from designing and maintaining a machine learning-minded product, and a product-minded machine learning system.
ACM award winners, leading researchers, industry veterans, thought leaders, and innovators address today and tomorrow's hottest topics and issues in computing for busy practitioners, as well as educators, students, and researchers. Check out our archive of these ACM Learning Webinars, free for members and non-members alike.
Talks from some of the leading visionaries and bleeding-edge researchers in AI/ML: Fei-Fei Li on visual intelligence in computers and ImageNet; Eric Horvitz on AI solutions in the open world; and Tom Mitchell on using ML to study how the brain creates and represents language.
Register now for the next ACM Learning Webinar, "Building a Culture to Support Inclusive Design," presented on Thursday, March 7 at 12 PM ET/9 AM PT by Jen Devins, Google Accessibility UX Lead and Nithya Sambasivan, Senior User Experience Researcher at Google. Eve Andersson, Director of Google AI and ACM Professional Development Committee Chair, will moderate the questions and answers session. Continue the discussion and checkout further resources on ACM's Discourse Page.
View the most recent ACM Learning Webinar, "The Bayesian Zig Zag: Developing Probabilistic Models Using Grid Methods and MCMC," on demand. The talk was presented by Allen Downey, Professor of Computer Science, Olin College. Continue the discussion and checkout further resources on ACM's Discourse Page.