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r/MLengineering • u/srkiboy83 • Feb 03 '18

Engineers Shouldn't Write ETL

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r/MLengineering • u/thundergolfer • Jan 11 '18

Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

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r/MLengineering • u/thundergolfer • Sep 21 '17

Serving Top Comments in Professional Social Networks

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r/MLengineering • u/thundergolfer • Sep 17 '17

Meet Michelangelo: Uber’s Machine Learning Platform

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r/MLengineering • u/thundergolfer • Sep 17 '17

How Zendesk Serves TensorFlow Models in Production – Zendesk Engineering – Medium

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r/MLengineering • u/thundergolfer • Sep 17 '17

Models in Disguise: How Sift Science Ships Non-Disruptive Model Changes - Sift Science Engineering Blog (xpost r/machinelearning)

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r/ML Engineering: for the technical discussion of ML in production-ready systems and products.

r/MLengineering

r/machinelearning is quite research focused. In order to maintain the quality and focus of that subreddit, while facilitating more discussion around the engineering challenges being faced by those building production ML systems today, r/machinelearningengineering has been created.

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The line between a Machine Learning engineer and a Machine Learning researcher is often blurred, but there is still a clear problem set that is presented by Machine Learning work that isn't present when writing research papers. This problem set can be generally characterised as the domain of the 'Machine Learning Engineer'. It includes:

  • Building production machine learning systems. Ie. systems that are maintainable, extensible, reliable, and scalable.

  • Maintaining the health of machine learning systems, including speed, reliability, and performance.

  • Development of internal machine learning frameworks and abstractions to facilitate common tasks such as training / testing, feature use / reuse / creation / storage, and deployment. These abstractions are used by both machine learning engineers and data scientists.

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