Systems in practice · 2021

Guiding the machine-learning lifecycle

Ease.ML organises machine-learning development into an eight-step process for domain experts with limited ML experience. The prototype connects data preparation, feasibility checks, model training and ongoing quality control, with people involved where their input is needed. It focuses on managing the whole workflow. The paper also identifies limitations, including its emphasis on prediction accuracy rather than fairness or explainability.

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Ease.ML workflow links automatic data preparation, feasibility analysis, data cleaning and acquisition, model training, testing and production model selection. — The Ease.ML process links data preparation, model development and ongoing quality control. Original Figure 1.
Aguilar et al. and CIDR 2021, Figure 1. Cropped from PDF page 3. Source · CC BY 3.0. Cropped the original PDF around the complete figure; rasterized without changing scientific content.
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Conference contribution

Ease.ML: A Lifecycle Management System for MLDev and MLOps

Leonel Aguilar, David Dao, Shaoduo Gan, Nezihe Merve Gurel, Nora Hollenstein, Jiawei Jiang, Bojan Karlas, Thomas Lemmin, Tian Li, Yang Li, Susie Rao, Johannes Rausch, Cedric Renggli, Luka Rimanic, Maurice Weber, Shuai Zhang, Zhikuan Zhao, Kevin Schawinski, Wentao Wu, Ce Zhang

11th Annual Conference on Innovative Data Systems Research (CIDR) · 2021

Summary based on CIDR 2021 published conference paper, official CIDR archive copy, PDF pages 1, 2, 3, 6.