Managing AI Products
summary
Covers what additional skills and mindset a Product Manager needs to deliver AI products, based on experience managing and advising multiple AI projects. Focuses on practical product management challenges for AI initiatives, drawing on work with conversational drip-marketing startups and PM roles at Cisco, Juniper and Samsung.
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chapters (27)
- 0:00Intro
- 1:53Inteligent Systems
- 3:49Decision Making
- 6:22Economic Value Created by Al
- 7:15Al Product Management
- 9:53Strategic Al Product Manager
- 12:13Data Network Effects
- 13:11Data acquisition strategies
- 16:18Observe Product Trends in the Al Market
- 18:44Cut through hype-focus on practical use cases
- 19:44Identify Problem
- 21:32The PAC Framework
- 22:48Al hierarchy of opportunities
- 25:09Customer and Data obsession
- 26:46Build usable products with simple Al model
- 28:42Breadth first approach Data/Pipeline/ Model
- 30:19Example Product Data/Pipeline/ Model
- 32:43Consider Model Explainability
- 34:16Iteratively build use cases with mapped Al models
- 35:48Agile Deep Learning
- 36:28Ensure product fails gracefully
- 37:25Understand the fundamentals
- 39:11Influencing across the matrix
- 50:03Other Considerations
- 51:22Al Monetization models
- 52:54Summary
- 53:14Resources
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