How is batch processing used in machine learning?

What is batch processing in machine learning?

Batch processing in machine learning refers to the technique of processing data in groups or batches rather than one at a time.

Why is batch processing preferred over real-time processing?

Batch processing is often preferred because it can be more efficient, allowing for the processing of large datasets at once, which saves time and resources.

Can you give me an example of batch processing in a machine learning model?

Sure! An example is training a neural network where the model is updated after processing a batch of training examples, rather than after each individual example.

What are the advantages and disadvantages of batch processing?

Advantages include efficiency and better resource utilization, while disadvantages can be latency and less responsiveness to real-time data.