Automated machine learning: State-of-the-art and open challenges

R Elshawi, M Maher, S Sakr - ar** and operating ML applications leads to a
variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software …

Towards unified data and lifecycle management for deep learning

H Miao, A Li, LS Davis… - 2017 IEEE 33rd …, 2017 - ieeexplore.ieee.org
Deep learning has improved state-of-the-art results in many important fields, and has been
the subject of much research in recent years, leading to the development of several systems …

Improving reproducibility of data science pipelines through transparent provenance capture

L Rupprecht, JC Davis, C Arnold, Y Gur… - Proceedings of the …, 2020 - dl.acm.org
Data science has become prevalent in a large variety of domains. Inherent in its practice is
an exploratory, probing, and fact finding journey, which consists of the assembly, adaptation …

[PDF][PDF] Ground: A Data Context Service.

JM Hellerstein, V Sreekanti, JE Gonzalez, J Dalton… - CIDR, 2017 - Citeseer
Ground is an open-source data context service, a system to manage all the information that
informs the use of data. Data usage has changed both philosophically and practically in the …

Vamsa: Automated provenance tracking in data science scripts

MH Namaki, A Floratou, F Psallidas… - Proceedings of the 26th …, 2020 - dl.acm.org
There has recently been a lot of ongoing research in the areas of fairness, bias and
explainability of machine learning (ML) models due to the self-evident or regulatory …