February 2021
Beginner
61 pages
1h 29m
English
Content preview from Product Management for AI
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Chapter 4. AI Product Management After Deployment
The AI product manager’s job isn’t over when the product is released. PMs need to remain engaged after deployment.
The field of AI product management continues to gain momentum. As the AI product management role advances in maturity, more and more information and advice has become available. Our previous chapters introduce our own take on AI product management, discuss the skills that AI product managers need, and detail how to bring an AI product to market.
One area that has received less attention is the role of an AI product manager after the product is deployed. In traditional software engineering, precedent has been established for the transition of responsibility from development teams to maintenance, user operations, and site reliability teams. New features in an existing product often follow a similar progression. For traditional software, the domain knowledge and skills required to develop new features differ from those necessary to ensure that the product works as intended. Because product development and product operations are distinct, it’s logical for different teams and processes to be responsible for them.
In contrast, many production AI systems rely on feedback loops that require the same technical skills used during initial development. Similarly, in Building Machine Learning Powered Applications, Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.