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  • November 2023
  • Case
  • HBS Case Collection

Open Source Machine Learning at Google

By: Shane Greenstein, Martin Wattenberg, Fernanda B. Viégas, Daniel Yue and James Barnett
  • Format:Print
  • | Language:English
  • | Pages:14
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Abstract

Set in early 2023, the case exposes students to the challenges of managing open source software at Google. The case focuses on the challenges for Alex Spinelli, Vice President of Product Management for Core Machine Learning. He must set priorities for Google’s efforts in open source communities. An important priority is the TensorFlow framework. Released by Google in 2014, it supports many advances in modern artificial intelligence and continues supporting it. Spinelli must also manage open source activities in additional areas, such as the Android operating system for mobile handsets and cloud computing. The choices and strategies have consequences for Google’s position as a technological leader among programmers who use and write code and for the safety and transparency of the code Google uses and supports. The case frames questions about what, if anything, Google should change with the explosion of interest in generative AI.

Keywords

Decision Choices and Conditions; Technological Innovation; Open Source Distribution; Strategy; AI and Machine Learning; Applications and Software; Technology Industry; United States

Citation

Greenstein, Shane, Martin Wattenberg, Fernanda B. Viégas, Daniel Yue, and James Barnett. "Open Source Machine Learning at Google." Harvard Business School Case 624-015, November 2023.
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About The Authors

Shane M. Greenstein

Technology and Operations Management
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Martin Wattenberg

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Fernanda B. Viégas

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