2022/12/09
ChatGPT and ML Product Management
Huh, look at that, OpenAI’s ChatGPT portrays absolute confidence while giving plain wrong answers. But ChatGPT also does provide helpful responses a large number of times. So one kind of does want to use it. Sounds an awful lot like every other machine learning model deployed in 2022. But really, how do we turn fallible machine learning models into products to be used by humans? Not by injecting its answers straight into StackOverflow.
2021/10/03
On Google Maps Directions
Google Maps and its Directions feature are the kind of data science product everyone wished they’d be building. It augments the user, enabling decision-making while driving. Directions exemplifies the difference between prediction and prescription. Google Maps doesn’t just expose data, and it doesn’t provide a raw analysis by-product like SHAP values. It processes historical and live data to predict the future and to optimize my route based on it, returning only the refined recommendations.
2021/08/12
What Needs to Prove True for This to Work?
Data science projects are a tricky bunch. They entice you with challenging problems and promise a huge return if successful. In contrast to more traditional software engineering projects, however, data science projects entail more upfront uncertainty: You’ll not know until you tried whether the technology is good enough to solve the problem. Consequently, a data science endeavor fails more often, or doesn’t turn out to be the smash hit you and your stakeholders expected it to be.