Two in five ‘AI startups’ essentially have no AI, mega-survey of nearly 3,000 upstarts found recently. Wait? What? Are they just pretending but not delivering?
In my talk I share lessons learned from my own experience as PM applying ML to solve user problems. ML based products differ significantly from traditional products – i.e. they may not always have deterministic behaviour, even produce counter-intuitive results and have different operational need. In short: they require a different mindset, different skills.
Getting the basics: What’s AI vs ML? When do I need ML?
Getting into the weeds: What does MVP mean for an ML based product? How do I operate an ML based product? What’s technical debt in the ML world?
Getting practical: How do I best present the results of fancy ML based predictions to users? Fairness for ML, what’s that? How do I collect user feedback?