Why: I did and thought of many projects where I could have used a very similar machine learning model by changing a few parameters. My hypothesis was that creating an API to use machine learning, would make it more accessible. One of the main use cases I thought of was to make it easy to let machine learning make design decisions like what copy or color to use for a certain button from many different options to maximize some function.
Tools: PyTorch (ML models), Python (backend), ReactJS (frontend)
Reason to quit: An average software engineer wasn't available to understand where ML can be used and data scientists didn't see the model creation as a big part of their job. People who understand the basics but are looking to prototype quickly could have seen this as a useful tool but they often needed more custom models.
What I learned: Importance of doing customer interviews before building anything. Offering free services is an easy way to get customers but the approach backfires as the customers become passive when they aren’t invested. In hindsight, it would have been a better approach to do something for a specific use case like a recommendation engine for grocery stores.