Product Management in ML /AI companies
For this article, I’m assuming that we’re not questioning whether ML / AI is the right solution that your organization is trying to address. But I do assume that an ML / AI company is building it themselves. Licensing an ML-based solution doesn’t make you an ML / AI product manager.
No domain and ML experience as a PM
Do you need to be a domain expert to work in product?
Let’s have a look at Jua.ai the product which I’m leading right now. We have an ML-based weather prediction model which claims to produce weather forecasts better than what the industry has otherwise.
We also deliver our product through an API to our typical B2B / Enterprise customers.
Let’s take away the Machine learning part for a second and let’s say we just produce weather forecasts. I’m not a weather expert at all and I didn’t feel at all that this held me back. I can still obsess over the outcomes of bad weather as a personal experience and how it’s all connected on a global scale. Will I ever be a meteorologist? Probably not. But I already learned in 4 months a ton about weather and climate.
If this was an ordinary product we would have our answer. No, you don’t. You might not even have to be technical as long as you understand the consumers extremely well.
Machine learning and being ‘technical’
Here’s where it gets tricky.
Machine learning is not just a different language in which programs run. It’s a completely different beast in how things are done from start to end.
It’s very powerful and complex. And chances are you have absolutely 0 clues on what’s going on inside of it all.
Let’s go back to the example. If we add ML to the mix we have a problem. I would be swimming if I wasn’t a technical person at Jua. I’m not a former ML engineer but I know how to develop software. I know it well enough to know intrinsically what problems come with it.
Nothing is ever simple and when something smells complex it’s probably going to break down. Improvements to the product are not happening like in other products. We’re not surfacing a user need and then in a week we have a “solution” or feature for it.
A PM not having any technical background in an environment like this would be like hiring someone that doesn’t know how to operate a laptop. You’re making it unnecessarily hard on yourself. You won’t understand the interconnected mess we’re in. The amount of data we deal with is massive, and the technology itself changes almost every week based on what’s written in research papers.
And you have a lot of different people and functions in the mix. It’s the classical problem a lot of hardware companies have:
There’s a different type of engineer for almost everything. The time when you could separate your engineers into frontend/backend is over.
The effect of it is you have many more breaking points due to more people involved in the entire process. If you ever thought that you have to be defensive in estimating efforts then you’re in for a surprise with an ML product.
There is no process or RICE framework that is going to save you from this, if you don’t have a sense of software architecture in some way you will be stumped by the additional complexity.
Connecting the ‘business’ side with a complicated offering
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