Building machine learning systems with high predictive accuracy is inherently hard, and embedding these systems into great product experiences is doubly so. To build truly great machine learning products that reach millions of users, organizations need to marry great data science expertise, with strong attention to user experience, design thinking, and a deep consideration for the impacts of your prediction on users and stakeholders. So how do you do that?

Today’s guest is Sam Stone, Director of Product Management, Pricing & Data at Opendoor, a real-estate technology company that leverages machine learning to streamline the home buying and selling process. Sam played an integral part in developing AI/ML products related to home pricing including the Opendoor Valuation Model (OVM), market liquidity forecasting, portfolio optimization, and resale decision tooling. Prior to Opendoor, he was a co-founder and product manager at Ansaro, a SaaS startup using data science and machine learning to help companies improve hiring decisions. Sam holds degrees in Math and International Relations from Stanford and an MBA from Harvard.

Throughout the episode, we spoke about his principles for great ML product design, how to think about data collection for these types of products, how to package outputs from a model within a slick user interface, what interpretability means in the eyes of customers, how to be proactive about monitoring failure points, and much more.

#134 Building Great Machine Learning Products at Opendoor 39:48 Share

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#134 Building Great Machine Learning Products at Opendoor