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Aroma: Using ML for Code Recommendation at Facebook

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@ -621,6 +621,7 @@ An organized reading list for illustrating the patterns behind scalable, reliabl
* [Data Visualisation Platform at Myntra](https://medium.com/myntra-engineering/universal-dashboarding-platform-udp-data-visualisation-platform-at-myntra-5f2522fcf72d)
* [Building and Scaling Data Lineage at Netflix](https://medium.com/netflix-techblog/building-and-scaling-data-lineage-at-netflix-to-improve-data-infrastructure-reliability-and-1a52526a7977)
* [Distributed Machine Learning](https://www.csie.ntu.edu.tw/~cjlin/talks/bigdata-bilbao.pdf)
* [Aroma: Using ML for Code Recommendation at Facebook](https://code.fb.com/developer-tools/aroma/)
* [Michelangelo: Machine Learning Platform at Uber](https://eng.uber.com/michelangelo/)
* [Scaling Michelangelo](https://eng.uber.com/scaling-michelangelo/)
* [Horovod: Open Source Distributed Deep Learning Framework for TensorFlow at Uber](https://eng.uber.com/horovod/)