Validate Business Impact of AI Features скачать в хорошем качестве

Validate Business Impact of AI Features

Check out all upcoming FREE workshops and LIVE courses: https://maven.com/dataneighbor Upcoming free workshops mentioned: Design Metrics that Don't Lie: https://maven.com/p/c532c6/design-met... Design Experiment for AI Features: https://maven.com/p/ba14ed/design-exp... Measure AI Impact with Causal Inference: https://maven.com/p/cddd45/measure-ai... Live 5-6 week courses mentioned: AI Evaluation for Product Development: https://maven.com/dataneighbor/ai-evals AI Analytics for Builders: https://maven.com/dataneighbor/ai-ana... Connect with us on LinkedIn (tell us YouTube sent you) Shane Butler:   / shaneausleybutler   Sravya Madipalli:   / sravyamadipalli   Hai Guan:   / hai-guan-6b58a7a   In this live workshop, Shane Butler (Principal Data Scientist, AI Evals) walks through a practical way to validate the business impact of AI features, not just whether “ai quality got better.” You’ll learn the AI Impact Chains framework and how to connect: Feature change → AI quality → customer value → business impact Along the way, we’ll cover: The 3-stage progression for AI product teams: Why AI quality metrics are not the same as customer value How customer value metrics break ties between competing quality metrics How experiments help you learn which quality metrics actually move customer value How causal inference translates customer value into revenue impact forecasts you can take to leadership Chapters 8:25 AI Development Progression Framework 15:28 Customer Value Metrics in AI 25:07 AI Revenue Attribution Framework 31:49 AI Feature Performance Comparison 40:57 AI Experiment Design Metrics 50:57 Causal Inference and Business Impact 57:54 AI Evaluation Challenges and Solutions Links and resources (copy/paste)