What are Python generators, and when would you use them in data preprocessing for ML?

Question

Khushi Singh · Answer

Python
Generators represent unique functions that produce iterators to support sequence value iteration without requiring total sequence loading into memory at startup. Generators deliver their values through the yield keyword rather than the complete list-return mechanism. The generator operation delivers benefits regarding memory usage making it a suitable tool for dealing with extensive datasets or continuous data flows.
and The ML data preprocessing technique benefits greatly from generators when operating on datasets which exceed the available memory. ML pipelines can break down data processing via generators which deliver data portions one by one thus reducing memory consumption during operation. The technique provides important support for deep learning model training because you frequently need to give the model data through small batches.and 
A generator system enables programmers to execute data preprocessing tasks such as normalization or reshaping or data augmentation while moving data to a model. These data processing tools enhance efficiency and adaptability in one system.and 
Through Keras in TensorFlow you can develop custom data generators by inheriting Sequence which runs multi-thread operations while automatically controlling batch sizes together with shuffle processing. The functional advantage of data generators becomes crucial during training models that work with extensive image collections or flow-based NLP text inputs. Generators enhance all three elements including scalability and maintainability and performance enhancement when applied to ML training operations.and 
Real-time data augmentation together with infinite data streams and prevention of memory overflows all work thanks to generators which make them essential elements in current machine learning workflows.
Example:
def data_generator(data, batch_size):
   for i in range(0, len(data), batch_size):
       batch = data[i:i+batch_size]
       yield preprocess(batch)  # apply some preprocessing here
and 
and

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What are Python generators, and when would you use them in data preprocessing for ML?

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