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Explore techniques for parallelizing Python code to overcome the Global Interpreter Lock (GIL) and enhance performance in data science pipelines. Learn how to speed up CPU-bound programs, data collection, pre-processing, and feature engineering tasks through various parallelization methods. Discover strategies to work with larger datasets and gain deeper insights by reducing execution times. This 31-minute PyCon US talk provides practical approaches to implement parallelism based on specific program requirements and desired outcomes.
