Helix: Holistic Optimization for Accelerating Iterative Machine Learning

no code implementations • 14 Dec 2018 • Doris Xin, Stephen Macke, Litian Ma, Jialin Liu, Shuchen Song, Aditya Parameswaran

Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved.

BIG-bench Machine Learning

Helix: Accelerating Human-in-the-loop Machine Learning

no code implementations • 3 Aug 2018 • Doris Xin, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song, Aditya Parameswaran

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 training, and post-processing steps -- via trial-and-error to achieve the desired model performance.

BIG-bench Machine Learning Structured Prediction

How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature

no code implementations • 27 Mar 2018 • Doris Xin, Litian Ma, Shuchen Song, Aditya Parameswaran

A quantitative characterization of iteration can serve as a benchmark for machine learning workflow development in practice, and can aid the development of human-in-the-loop machine learning systems.

BIG-bench Machine Learning