Show notes
Ted tells us about applying machine learning to the field of baseball cards! 33% of Americans have trading cards, making this a very large addressable market. Learn some tips on scrappy ways to launch an app, and how similarity search powers one of the killer features of the CollX app. Key Moments: Building an application that works around the potential errors of an ML model (15:10). The data and ML behind his trading card valuation model, especially when recent transactions don’t exist. (18:30). Dealing with the latency inherent in ML and networking through the concept of “building lists” (18:25). Early work on product search (24:00). Working with bad training data and adding a “wizard behind the curtains” to deliver value while labeling data (26:18). More UX techniques to reduce perceived latency (28:00). Helping users understand that ML models are not 100% accurate (30:45). Advice for entrepreneurs trying to launch an app (35:20). A video version of this episode with visuals is available at https://www.youtube.com/watch?v=RX9xIYnn2v4. To learn more about this podcast, visit https://yaoshiang.com/podcast.html
