Machine Learning’s deployment stack is maturing
Exploration and insight on topics that sit at the intersection of business and technology.
It's Time to Look More Closely at Regulation
Do’s and Don'ts when Designing for the Community
Authentication, Backups, Updates, and Least Privilege
Where user-centric computing goes wrong
The code that holds our systems together
Labeling training data is the one step in the data pipeline that has resisted automation. It’s time to change that.
How do we build devices that are shared by default?
Risks of autoregressive language models and the future of prompt engineering
Four ways the party may be coming to an end
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?
How automation is likely to change professional software development.
Getting curious about the numbers attached to other people can help us to use data wisely—and to see others clearly.