Batch learning is the training of ML models in batch. As the data grows, training the model takes more time and resources.
In online learning, the model learns incrementally on a small group of observations instead of an entire dataset.
Thus, each learning step is fast and cheap, which makes it ideal:
- For applications that change rapidly
- For companies with limited computing resources.
In my latest article, you will learn how to use River to do machine learning on streaming data.
