Data Management Challenges for Deep Learning
Paper i proceeding, 2019

© 2019 IEEE. Deep learning is one of the most exciting and fast-growing techniques in Artificial Intelligence. The unique capacity of deep learning models to automatically learn patterns from the data differentiates it from other machine learning techniques. Deep learning is responsible for a significant number of recent breakthroughs in AI. However, deep learning models are highly dependent on the underlying data. So, consistency, accuracy, and completeness of data is essential for a deep learning model. Thus, data management principles and practices need to be adopted throughout the development process of deep learning models. The objective of this study is to identify and categorise data management challenges faced by practitioners in different stages of end-to-end development. In this paper, a case study approach is employed to explore the data management issues faced by practitioners across various domains when they use real-world data for training and deploying deep learning models. Our case study is intended to provide valuable insights to the deep learning community as well as for data scientists to guide discussion and future research in applied deep learning with real-world data.

Data Management

Deep learning

Deep Neural Networks

Artificial Intelligence

Machine Learning

Författare

Aiswarya Raj Munappy

Göteborgs universitet

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Psysical Systems

Jan Bosch

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Psysical Systems

Göteborgs universitet

Helena Holmström Olsson

Malmö universitet

Anders Arpteg

Peltarion AB

Bjorn Brinne

Peltarion AB

Proceedings - 45th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2019

140-147 8906736

45th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2019
Kallithea, Chalkidiki, Greece,

Ämneskategorier

Annan data- och informationsvetenskap

Lärande

Systemvetenskap

DOI

10.1109/SEAA.2019.00030

Mer information

Senast uppdaterat

2019-12-16