As software organizations race to add data science, machine learning, and AI to their portfolios, product managers find themselves with a daunting task, learning how to craft these technical models into viable products. How do you manage a backlog for an experimental science? How do you perform acceptance testing when you don’t know the outputs? How do you decide when your product is safe to release?
In this webinar, we’ll go through the mindsets and techniques product managers can apply when working with machine learning models as products. We’ll highlight areas where you can adapt your existing product mindset to the ML world and the risks and considerations you’ll need to bring into your practice.