Marily Nika: commitment

Transcript context

…Awesome. And is there anything else you want to share from your course that you think might be interesting to folks? So we talked about why it's awesome to be an AI PM, but I do want to call out that there are a few challenges that people need to be aware of. Number one, and I kind of mentioned it before, is the uncertainty. You may have been working on all of these incredible research and ideas in hypothesis, but then when you actually train the model, the results you may be getting may not be optimal, may not be answering the questions or the hypothesis that you actually had in mind. So that's number one. You need to be able to encourage the teams throughout this process because you're like the captain of the ship, you need to be the one that's cheerleading the team, making sure everyone keeps going. Number two, you are going to have to [inaudible 00:30:15]. You are going to have to change the action in managing this from a leadership perspective can be tricky and it can be challenging. Number three, we talked about data, but getting good data is hard. You may need to be creative, figure out ways for data collection that you never thought you'll do. You may get on the street and ask for people to actually contribute data for what it is you're doing. You need to be able to and willing to do everything. And the last thing is from a career trajectory, usually product managers get ahead the more they launch. But if you're in a research or if you're not going to launch as often, so you need to make sure to clarify with the hiring managers early on, "Hey, what does progress mean? How am I going to get a sense in a research work which is different than what I've been doing so far." So it's challenging but I always encourage people to flex different muscles and this is the zero to one muscle. The other thing is this crucial when it comes to product management. This actually is a great segue to a question I definitely wanted to ask, which is around getting buy-in for investment at a company for ML. So there's sometimes all this energy for a zero to one, let's just try something, sometimes not, so maybe there's a two part question here. Do you have any advice on just getting buy-in for we want to try something with ML. It's going to take us six months to figure out if it's worth the effort, but we think there's something here. And then sometimes there's a lot of energy initially and then you get some win, like your search ranking's smarter and it's great. But then maintaining that, having all these really expensive people working and just tweaking this model and continuing to make it smarter and a little more efficient, often it's hard to continue to get buy-in for that sort of team. Do you have any advice on initial buy-in, let's try something here, and then down the road just keeping a team going, trying to make this thing smarter and smarter.…

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