In this article, I will walk you through a complete Machine Learning Product Lifecycle and describe a Product Manager’s role in it. I do not define Product Management in too much detail. As a PM of AI-ML products, one needs to do everything that a PM does and, in addition to that, take care of specific AI/ML-related duties, which are described in this article.
The article also doesn’t dwell on different applications of ML. It assumes that the PM or a business leader has identified an application and wants to apply ML to it. I rather emphasize that finding a “real” problem is one of the most critical tasks for a PM in a Machine Learning project.
Lastly, I have aimed to be comprehensive yet consumable so that readers don’t save it on their digital memories and never get to it. Hence a few basic items will not be defined here; a quick google or a GPT search will suffice. :)
Contents:
1. What is Product Management
2. PM in ML/AI — Be Strategic
3. PM responsibilities in complete ML Life Cycle
Scoping → Data → Modeling → Validation → Deployment
4. Additional comments
5. Tools and techniques