Member-only story
Batch vs. Online Learning: Which Approach Fits Your Machine Learning Needs? (Part 2)
This blog post continues the discussion from my previous blog on batch learning, where I explained how models undergo training on large datasets in one go. In this post, we’ll explore online learning, a more dynamic approach to model training, and examine how it addresses some of the limitations of batch learning. Both methods have pros and cons, and understanding them will help you choose the right one for your machine learning needs.
Online Machine Learning
Online learning, also known as “Incremental Learning”, trains models in a much more dynamic fashion. Instead of being fed the entire dataset all at once, the model receives data continuously, learning from it one instance at a time. This allows it to update in real time as new data comes in, making it highly adaptable to rapidly changing environments.
