Data to Delight: Creating ML Products People Love

Successful machine learning products are not defined by models alone—they are defined by experience. Data to Delight is a machine learning book devoted to building ML-powered products that users actually enjoy, trust, and return to.
The writing traces the journey from raw data to meaningful user value. It explores how predictions become features, how systems learn from interaction, and how design choices shape perception. Machine learning here is treated as a product capability, not a technical showcase.
Rather than prioritizing accuracy in isolation, the book emphasizes usefulness. Readers learn how latency, explainability, feedback loops, and failure handling directly affect user satisfaction. Each chapter connects ML decisions with product design, usability, and emotional response.
The tone is practical and user-centered, aimed at product managers, engineers, and designers working together. Language remains clear and actionable, bridging the gap between technical development and human experience.
Data to Delight moves through product discovery, model integration, experimentation, iteration, and measurement—showing how ML creates value only when it serves people well.
Key themes explored include:
• ML as a product feature
• User-centric model design
• Trust and explainability
• Feedback-driven learning
• Delight through reliability
Data to Delight is for teams building ML products—offering a guide to turn data into experiences people genuinely love.