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You are here		tomaugspurger.net Scalable Machine Learning (Part 3): Parallel \| Tom's Blog
\|	\|	hypernephelist.com Data preparation at scale on Azure Machine Learning using Dask	5.9 parsecs away Travel
\|	\|	In a recent project, we worked with our customer on a sustainability project whose goal was to leverage geospatial and climate data to build a platform to pe...	5.9 parsecs away Travel
\|	\|	pythonspeed.com When Python can't thread: a deep-dive into the GIL's impact	4.5 parsecs away Travel
\|	\|	Python's Global Interpreter Lock (GIL) stops threads from running in parallel or concurrently. Learn how to determine impact of the GIL on your code.	4.5 parsecs away Travel
\|	\|	matthewrocklin.com PyData and the GIL	2.1 parsecs away Travel
\|	\|	[AI summary] The article explains how most PyData projects leverage native C/Fortran code to bypass Python's Global Interpreter Lock, enabling effective shared-memory parallelism on multi-core machines.	2.1 parsecs away Travel
\|	\|	isaacslavitt.com Isaac Slavitt: Solving the Bayesian German Tank problem with PyMC and PyStan	22.5 parsecs away Travel
\|		[AI summary] A Python developer solves the German Tank Problem using Markov Chain Monte Carlo methods, comparing implementations in Sampyl, PyMC3, and PyStan.	22.5 parsecs away Travel