How would you explain the concept of overfitting to a non-technical team member?

Overfitting is like when you study so hard for a test on historical dates that you can remember every date perfectly, but then you get a question on the test that asks you to explain the causes of an event, and you can't answer because you only focused on the dates. In machine learning, overfitting happens when our model learns the details and noise in the training data to an extent that it negatively impacts the performance on new data.

Imagine you've memorized your favorite recipe so well that you can cook it perfectly in your own kitchen. But if you were to cook the same dish in a friend's kitchen, you might struggle because the tools and ingredients are slightly different. In machine learning, overfitting is like knowing your recipe by heart but being unable to adapt to a new kitchen. It means our model performs really well with the data it was trained with, but it may not do well with new data it hasn't seen before. It's why we need to build our models to be flexible so they can make good predictions even when the data changes a bit.

Consider when you learn a song entirely by heart, right down to the timing and the pitch, and you perform it flawlessly at home. But when you're asked to sing at a friend's party, you falter because the background music is a bit different, and you were too focused on the version you practiced with. That's overfitting. In our machine learning projects, we want to avoid this by creating models that perform well not only on our practice data but also when they sing in various environments - meaning they can handle new, unseen data. As a Machine Learning Engineer, it's my job to ensure our models are like versatile musicians; they can still perform the hit song even if the accompaniment changes. Utilizing techniques from our toolkit like cross-validation, pruning, or regularization helps us to generalize our models for better performance across different datasets.