Episode description

Open Source software such as scikit-Learn, Python, and Spark form the backbone of data science. In a two-part series, we’re covering the ins and outs of open source - and how this special type of software supports 98% of enterprise-level companies’ data science efforts.

In part 1, we’re chatting with Andreas Mueller, a core contributor of  scikit-Learn aboutthe  value in open source versus corporate software, and what it looks like to run and govern this type of community-written (and driven) project.

Join our Paris scikit-Learn sprint this January: https://github.com/scikit-learn/scikit-learn/wiki/Paris-scikit-learn-Sprint-of-the-Decade

Andreas Mueller is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to Machine Learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and he has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, he worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. You can find his full cv here. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.