DataFramed #134 Building Great Machine Learning Products at Opendoor
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Apr 17, 2023 Sam Stone, Director of Product Management at Opendoor, shares his expertise in creating impactful machine learning products in real estate. He discusses the importance of marrying data science with user experience for successful deployment. Stone emphasizes the critical role of understanding user behavior and feedback in enhancing product effectiveness. He also highlights the necessity of clear North Star metrics and proactive strategies to navigate data challenges and ensure continuous improvement in ML systems.
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Cross-Functional Collaboration
- Build cross-functional partnerships between data science, engineering, and other relevant teams.
- Avoid the "throw-it-over-the-wall" approach where data science prototypes models and then hands them off to engineering without ongoing collaboration.
Key Elements of Great ML Products
- Great machine learning products stem from a deep understanding of user behavior, optimizing accuracy for user needs, and incorporating feedback loops.
- This approach ensures the product is user-centric and continuously improving.
Data Discrepancies and User Behavior
- Opendoor uses multiple data sources for home information, like square footage, which can sometimes conflict.
- Understanding user behavior helps reconcile these discrepancies, such as recognizing sellers inflate square footage while tax assessors minimize it.