Batch and online learning are two different methods of supervising machine learning algorithms.
**Batch Learning:**
Batch learning is a technique of processing large amounts of data to train the algorithm at once. In batch learning, the complete dataset is processed at once to train the model; hence it takes more time to train. We can’t get intermediate results of predictions because the algorithm works on the complete dataset. Batch learning requires more resources and larger memory, making it difficult to deal with large data sets.
In batch learning, the algorithm doesn’t learn from the updated data once it is trained; to update or improve the model, we have to train it again using all data.
**Online or Incremental Learning:**
Online learning is a technique of processing data by updating the model with each new data sample that arrives, and thus, it is also called incremental learning. It processes the data on the go, making it suitable for large data sets, and it requires considerably fewer computational resources.
In online learning, the model receives each data instance sequentially, and after each processing, the model updates itself. This way, we can get intermediate results, which can be useful in applications where processing time is a concern. Online learning algorithms are also suitable for situations where data evolves over time.
One of the most prominent application examples of online learning is adaptive personalization — the ability of a recommender system to evolve with user interactions. For example, the Netflix recommendation engine is a good example of online learning.
**Comparison:**
The main difference between batch and online learning is that batch learning processes the entire data set at once, whereas online learning processes small amounts of data, an instance at a time. Batch learning is computationally expensive, and we require more memory to process the data, whereas online learning is suitable for processing large datasets using fewer resources. Batch learning is efficient when training data is static, whereas online learning can continuously learn from updated data.
To summarize, batch learning is best suited for applications where we have a static dataset, and the training time that takes longer is acceptable. Online learning is preferable for applications where data is continuously updated and training time must be as short as possible.