961 citations · 1k across the 7 of their papers we have counts for
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Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development
Angela Lee, Doris Xin, Doris Lee +1
It is well-known that the process of developing machine learning (ML) workflows is a dark-art; even experts struggle to find an optimal workflow leading to a high accuracy model. U…
Helix: Accelerating Human-in-the-loop Machine Learning
Doris Xin, Litian Ma, Jialin Liu +3
Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model train…
How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature
Doris Xin, Litian Ma, Shuchen Song +1
Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evide…
MLlib: Machine Learning in Apache Spark
Xiangrui Meng, Joseph Bradley, Burak Yavuz +13
Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark's ope…