AI Product Management
The discipline of managing AI-powered products, which requires understanding both traditional product management and the unique characteristics of AI systems (uncertainty, data dependency, continuous learning).
Why It Matters
AI PMs need to manage user expectations around AI imperfection, design feedback loops, and make tradeoffs between model quality, latency, and cost.
Example
An AI PM deciding whether to show confidence scores alongside AI recommendations, how to handle hallucinations gracefully, and when to escalate to human agents.
Think of it like...
Like product management for a service with human employees — you need to manage both the technology and the 'judgment' it exercises, including how to handle mistakes.
Related Terms
Deployment
The process of making a trained ML model available for use in production applications. Deployment involves packaging the model, setting up serving infrastructure, and establishing monitoring.
Evaluation
The systematic process of measuring an AI model's performance, safety, and reliability using various metrics, benchmarks, and testing methodologies.
Responsible AI
An approach to developing and deploying AI that prioritizes ethical considerations, fairness, transparency, accountability, and societal benefit throughout the entire AI lifecycle.