How can I parallelize data preprocessing in my machine learning pipeline?

How can I parallelize data preprocessing in my machine learning pipeline?

You can use libraries like Dask or joblib in Python to parallelize your data preprocessing tasks. Would you like to see an example?

Yes, please provide an example using Dask.

Sure! First, install Dask using pip install dask. Then, you can use it like this:

import dask.dataframe as dd

df = dd.read_csv('your_data.csv')
processed_df = df.map_partitions(your_preprocessing_function)
result = processed_df.compute()

This will distribute the preprocessing across multiple cores.

What if I have a large dataset that doesn't fit into memory?

In that case, Dask is particularly useful because it handles out-of-core computation. It processes data in chunks, allowing you to work with datasets larger than your RAM.