AI, ML or DS...whatever it is, I want it.

Photo by Green Chameleon on Unsplash
  • We are not Google or Apple. Our team are nubes, even if you have just hired that amazing CTO or VP engineering or (insert cool techie title here). We still need to ramp up. Unless you have the stomach to lay out the cash to hire the entire vertical from one of the big guns in this business, then that’s just the way it is. Even when you hire in, we are integrating them into an existing team that don’t have the knowledge base you just acquired. I could tell you stories of long hours trying to convince people to do things differently in order to put ML into product.
  • We are going to make mistakes. Sometimes the mistakes will happen before we integrate it into the product, sometimes after. And because we have an iterative product development process, v1 is just not going to be that spectacular when we finally get it working. Truth.
  • Machine learning is research, our problem is not already solved. Many things will be tried that do not yield the desired results. Yes, there are tools and pre-trained models that we can leverage. But a lever provides an advantage to an exerted effort, it does not in and of itself provide the solution. Let me tell you about the frustration that I have heard at the amount of time taken to get to the first model.
  • Machine learning is not the answer to every problem. Sometimes the customers’ problems can be solved with something simpler. Unnecessary complexity comes at a cost. I would recommend you think about that when you are looking at your balance sheet.
  • Other product features still matter. Our customers won’t give a shit about the fancy backend if the product is difficult to use or the results are unreliable. One of the first things I learned as a PM is that it is better to have a kick-assed half product than half-assed complete product.
  • While data driven machine learning can be more powerful than traditional coded solutions, it is more complex to maintain. Model development is not one and done, every model degrades. Let me tell you how that happens.
  • Have you thought about pushback? Not every customer or industry is keen on having their data mined, even though you provide a DUA. What are the laws around data use that we need to think about? What about transparency/explainability? The existing data governance in the organization will need to change to this new usage. Please don’t leave these important items to your technical team.

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Tyna Hope

Tyna Hope

An ML product manager. Bio on LinkedIn. Opinions expressed are my own.