Workshop: Product development for identifying and leveraging ML opportunities
summary
Argues that most AI effort should be product-first not model-first, backing it with time breakdowns (40–50% data, 30–40% modeling + deployment, 10–30% problem selection) and a claim that ~87% of AI projects fail. Gives a concrete workflow: generate many ideas in hours, validate mock-ups in days, build prototypes in weeks, code for months, ship a product in a quarter; shows an idea → mockup → prototype → program → product pipeline, live data prototypes and metrics-driven experiments (example: image classifier versus continuous monitoring for targeted pesticide use), and recommends specific team roles and architecture checks before scaling.
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chapters (24)
- 0:00Start
- 7:45What Is the Difference between an Ai Product and an Ai Project
- 9:10Success for a Product Is Measured by Realized Value
- 14:31The Ethical Gray Zones
- 18:57Prototypes
- 20:31Time Scales
- 23:06Invalidation Framework
- 23:28Validation Framework
- 26:18Abstraction Ladder
- 36:07Discovery
- 38:32Feasibility
- 41:09The Product Strategy
- 42:04Product Features
- 43:19What Are the Common Mistakes in Identifying Revenue Generating Ml Opportunities
- 44:59Short-Term Validation
- 46:00Feasibility Prototype
- 46:58Live Data Prototype
- 48:03Viability Prototype
- 51:17Product Team
- 51:42What Kind of Product Team You Need
- 52:34Art of Pivoting
- 56:48Model Management
- 59:16Implicit Feedback
- 59:37Book Recommendations
