After training your model, you will realize that your model is static by nature, that is, once the model is trained and deployed, it will not change. However, data is dynamic in nature, that is, new data will continue to be available after your model was deployed.

So, How to make your model to continue learning with the new data coming in?

If you have a very large dataset, without Hadoop or Spark, you won't be able to train your model. What happens if the data coming is Streaming data from the web (like comments, post reactions, etc)?

To continuously update your model with new data, you use Updatable Classifiers (also called Online Learners or Incremental Classifiers) from the Sklearn library.  Updatable Classifiers split data in chunks. This way your model can continuously learn chunk at a time.