Open Problems in Engineering and Quality Assurance of Safety Critical Machine Learning Systems

Kuwajima, Hiroshi; Yasuoka, Hirotoshi; Nakae, Toshihiro

doi:10.48550/arxiv.1812.03057

Search citation statements

Order By: Relevance

Paper Sections

Select...

Feasibility1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Requirements on the application: The minimal requirements of the applications should be defined as an input for the subsequent phases. Requirements could be, for example, the inference time of a prediction, the memory size of the model (considering it has to be deployed on hardware with limited memory), the performance and the robustness of a model or on the quality of the data (Kuwajima et al, 2018). The challenge during the development is to optimize the success metric while not violating the requirements and constraints.…”

Section: Feasibilitymentioning

confidence: 99%

Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

Studer¹,

Bui²,

Drescher³

et al. 2020

Preprint

View full text Add to dashboard Cite

We propose a process model for the development of machine learning applications. It guides machine learning practitioners and project organizations from industry and academia with a checklist of tasks that spans the complete project life-cycle, ranging from the very first idea to the continuous maintenance of any machine learning application. With each task, we propose quality assurance methodology that is drawn from practical experience and scientific literature and that has proven to be general and stable enough to include them in best practices. We expand on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks.

show abstract

Section: Feasibilitymentioning

confidence: 99%