In Part 1 of this series we covered the fundamentals that every AI product manager needs to know. Part 2 covered team management and the ML model development process. Now it’s time to select the best model and deploy it to production.
Choosing the correct machine learning model for production deployment is a tradeoff between model accuracy, size and inference time (inference is the process of the model generating results). Balancing the tradeoff depends on what your feature or application needs to do for the user.
As a PM you need to understand this tradeoff and consider the right balance…
In Part 1 we covered the groundwork an AI PM needs to do. In Part 2 let’s start with how to manage your ML team.
In addition to your typical application development team structure, you will need engineers dedicated to ML development and deployment. The exact number, skillset and experience depends on the project specifics. You can either have ML engineers and application developers in 2 units within a single team or as 2 separate teams. In either case, both should work parallelly but certainly not in silos. …
A vast number of software applications ranging from YouTube’s recommendation algorithm to Apple’s Face ID use Machine Learning (the methodology behind implementing AI) behind the scenes. Adoption of AI in every single industry is increasing so rapidly that AI is forecasted to contribute about $15.7 trillion to the world economy and boost local economies by 26% by 2030.
I'm a Product Manager specializing in building AI and AR products. I write about technology and startups.