AI & ML
Few technologies have the potential to change the nature of work and how we live as artificial intelligence (AI) and machine learning (ML).
Asking very simple questions often leads to discussions that give much more insight than more complex, technical questions.
The AI product manager’s job isn’t over when the product is released. PMs need to remain engaged after deployment.
Identifying AI opportunities and setting appropriate goals are critical to AI success, and yet can be difficult to do in practice. Some reasons for this include lack of AI literacy, maturity, and many other factors.
All-in-one platforms built from open source software make it easy to perform certain workflows, but make it hard to explore and grow beyond those boundaries.
Previous articles have gone through the basics of AI product management. Here we get to the meat: how do you bring a product to market?
Data is often biased. But that isn’t the real issue. Why is it biased? How do we build teams that are sensitive to that bias?
A Bad Outcome Doesn't Mean a Bad Decision
Getting curious about the numbers attached to other people can help us to use data wisely—and to see others clearly.
Companies that succeed will protect, fight for, and empower their users
A product manager for AI does everything a traditional PM does, and much more.
Your models are only as good as your data.