Using ensemble pattern techniques

In this section, we will discuss different types of ensemble approaches along with examples. We have seen that we can combine the models in five types of different scenarios. The following subsections will discuss them one by one.

Model update

In the machine learning (ML) deployment life cycle, updating the model happens regularly. For example, we might have to update a model for route planning if new roads and infrastructure are built or removed. Whenever a model needs to be replaced, it might be risky to replace the current model directly. If for some reason, the new model performs poorly compared to the previous model, then it might cause critical business problems and loss of trust. For example, let’s imagine we have updated a model with a new version tag, V2, that predicts a stock price. The V1 model version was predicting stock prices with an MSE of 10.0. Although during training the V2 model was performing very well, in production...