How to Build AI Products by Microsoft Group Product Manager
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Main takeaways: - Understanding “FATE” - Fairness, Accountability, Trust and Ethics in the context of AI -How bias impacts data in AI and how to prevent it -What ethical design principles are and how to embed them in an AI system -How to build trustworthiness in AI
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How to Build AI Products by Microsoft Group Product Manager
- 1. www.productschool.com How to Build AI Products by Microsoft Group Product Manager
- 2. FREE INVITE Join 30,000+ Product Managers on
- 3. COURSES Product Management Learn the skills you need to land a product manager job
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- 9. Building AI Products Ria Sankar Product Management @Microsoft
- 11. AGENDA Source: https://thefuturesagency.com/ • Typical AI Product Lifecyle • Types of AI Products • Closing & Discussion
- 13. A typical product lifecycle Plan DefineExecute User Research: Identify underlying user needs Frameworks: Structure and frame problem spaces Metrics: Prioritize features, Validate hypotheses 13 2
- 14. AI product lifecycle Plan DefineDeploy • User & Business Understanding • Problem & Metric Definition • Data Preparation • Model Evaluation • Model Deployment • Ongoing Measurement & Learning 13 2
- 15. Plan DefineDeploy • User & Business Understanding • Problem & Metric Definition 1 Plan
- 16. Find your niche Customer DataBusiness NicheArticulate AI value in terms of: • Agility / Performance / Cost • Growth drivers • Brand value / Industry Status • Risk reduction • Accessibility • Customer Delight • Convenience/usability 0
- 17. Customer Understanding: Jobs Theory Seeing tasks from a customer vs. product context 1a Functional “Help me wake up with the best coffee at consistent quality” Social “Give me a place to connect with my friends” Emotional “Help me treat myself at the end of a long day”
- 18. Business Consequences: Building Trust Use FATE Principles 1b https://www.microsoft.com/en-us/ai/our-approach-to-ai
- 19. CASE STUDY: Travel Chatbots (Mezi, Expedia) https://www.altexsoft.com/blog/business/chatbots-in-travel-how-to-build-a-bot-that-travelers-will-love/
- 20. Data Understanding: Preventing Bias1c https://i.imgflip.com/1w3emg.jpg • Comprehensive test cases (represent the real world) • Data stratification • Diverse workforce (avoid tech bro AI) • Unconscious bias – review model outputs for correlations to race and gender
- 21. Metric Understanding: Problem & Output Definition 1d https://i.imgflip.com/1w3emg.jpg • Instrumentation • Data Quality • Primary vs. secondary goals • Product vs. Feature • Standard vs. derived metrics
- 22. Discussion: How would you improve YouTube’s Recommendation Engine?
- 23. Plan DefineDeploy Define Data Prep, Model Evaluation2
- 24. Data Preparation 4Cs of data quality • Correct • Conforms • Current • Consistent • Consolidated 2a http://4.bp.blogspot.co m/
- 25. Designing AI 5 principles of ethical design • Humans as Heroes • Honor Diversity • Balance EQ and IQ • Know context • Evolve over time 2b https://www.microsoft.com/en-gb/ai/our-approach- to-ai "The AI tools and services we create must assist humanity and augment our capabilities." —Harry Shum, Executive Vice President, AI and Research
- 26. Model Selection https://devzum.com/wp-content/uploads/2015/05/best-machine-learning-cheat-sheets3.jpg https://azure.microsoft.com/en-us/blog/announcing-automated-ml-capability-in-azure-machine- learning/?WT.mc_id=azure-presentation-lazzeri Other Resources: https://docs.microsoft.com/en-us/azure/machine-learning/studio/media/algorithm-cheat- sheet/machine-learning-algorithm-cheat-sheet-small_v_0_6-01.png 2c Factors influencing model complexity and data selection: • Supervised vs. Unsupervised • Batch vs. Online • Classification vs. Predicting value
- 28. Model Deployment • Scale data collection • Scale scenario coverage • Action movie recos for all users vs. Movie recos for subset of users • Scale model • Check outliers and bias • Visualize outputs • Model specific 3a https://cloud.google.com/auto https://cloud.withgoogle.com/next18/sf/sessions/session/193072 https://www.youtube.com/watch?v=GbLQE2C181U
- 29. Ongoing learning: Uber Michelangelo Platform https://eng.uber.com/michelangelo/ 3b
- 30. Case Study: Uber Eats
- 31. Types of AI Systems
- 32. Getting started with AI https://lexfridman.com/stephen- wolfram/
- 33. AI Progression: Balancing interactions between humans and AI https://medium.com/@davidbessis/building-ai-first-products-
- 34. Pre-Engine vs. Engine-Inside vs. Engine-First https://medium.com/@davidbessis/building-ai-first-products-
- 35. Pre-AI
- 38. Discussion: How would you build an AI experience for a restaurant?
- 39. RECAP: CRISP-DM Methodology Business & User Understandin g Data & Metric Understandin g Data Prep Model Development Model Evaluation Deployment 1. Scope your problem 2. Build the business case for ML / AI 3. Select your ML model 4. Balance model performance and accuracy 5. Ensure model relevancye to changing business needs 6. Human powered vs. machine AI
- 42. Q&A
- 43. Thank you
- 44. www.productschool.com Part-time Product Management, Coding, Data, Digital Marketing and Blockchain courses in San Francisco, Silicon Valley, New York, Santa Monica, Los Angeles, Austin, Boston, Boulder, Chicago, Denver, Orange County, Seattle, Bellevue, Toronto, London and Online