Product Management with a Data Science Mindset.

I see myself as working in the center of these spaces.
  • What’s different about ML? In the most general terms, for supervised learning solutions, the situation is Data + expected output = code. A learning framework is leveraged to produce a savable object which can infer an output value from inputs. The model is based on training data, as well as the learning algorithm and its parameters which control what can be codified in the resultant model.
  • What should I know? Basic concepts and the ML language so that you understand the issues during model design and integration in the product. There are lots of great on-line courses and lectures are often posted on YouTube. If you have any coding knowledge, take a basic course that will have you create a simple model (e.g. simple linear regression). You are not trying to become a DS but a couple courses that explain training, validation, bias, variance, and performance metrics will go a long way to making your life easier.
  • What can go wrong? Several things can go wrong. Here are just s few: The model can be designed that is biased. The integrated model has a shelf life and will become stale, maybe without you knowing. The model doesn’t address the correct problem. The list can go on…
  • What’s great about it? There can be a lot of hype about ML. With the right data (type and amount), ML can reveal patterns and relationships that would be very difficult if not impossible for a human to find. There is no need for a human to identify all of the rules, that’s pretty great.
  • This is research. There is no guarantee that a solution can be found that meets your desired performance criteria. Be willing to re-think the problem and iterate.
  • Monitoring is not optional. MLOps is a whole field unto its self but I have found that the single most under valued component of operationalizing ML solutions is the monitoring. Until you need it.

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Tyna Hope

Tyna Hope

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