A couple of years ago, Pete Skomoroch, Roger Magoulas, and I talked about the problems of being a product manager for an AI product. We decided that would be a good topic for an article, and possibly more.

After Pete and I wrote the first article for O’Reilly Radar, it was clear that there was “more”–a lot more.  We then added Justin Norman, VP of Data Science at Yelp, to the team.  Justin did the lion’s share of the work from that point on.  He has a great perspective on product management and AI, with deep practical experience with real-world products: not just building and deploying them, but shepherding them through the process from the initial idea to maintaining them after employment–including interfacing with management.

Many organizations start AI projects, but relatively few of those projects make it to production.  These articles show you how to minimize your risk at every stage of the project, from initial planning through to post-deployment monitoring and testing.  We’ve said that AI projects are inherently probabilistic. That’s true at every stage of the process.  But there’s no better way to maximize your probability of success than to understand the challenges you’ll face.


Product Management for AI


What you need to know about product management for AI
Practical Skills for the AI Product Manager
Bringing an AI Product to Market

Related work from others:  Latest from MIT Tech Review - Responsible technology use in the AI age

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