Welcome back to the Illustrated Machine Learning series. If you read the other articles in the series, you know the drill. We take a (boring sounding) machine learning concept and make it fun by illustrating it! This article will cover a concept called Incremental Learning, where machine learning models learn new information over time, maintaining and building upon previous knowledge. But before getting into that, let’s first talk about what the model building process looks like today.
We usually follow a process called static learning when building models. In this process, we train a model using the latest available data. We tweak and tune the model in the training process. And once we’re happy with its performance, we deploy it. This model is in production for a while. Then we notice that the model performance is getting worse over time. That’s when we throw away the existing model and build a new one using the latest available data. And we rinse and repeat this same process.
Let’s illustrate this using a concrete example. Consider this hypothetical scenario. We started building a fraud model at the end of January 2023. This model detects whether a credit card transaction is fraudulent or not. We train our model using all the credit card transaction data that we had from the past one-year period (January 2022 to December 2022) and use transaction data from this month (January 2023) to test the model.
At the end of next month we notice that the model isn’t doing too well against new data. So we built another model, but this time using data from the past one-year period (February 2022 to January 2023) to train it and then use the current month’s data (February 2023) to test it. And all data outside of these training and testing periods is thrown out.