AI Technical Considerations: Data Storage, Cloud usage and AI Pipeline
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Abstract (arXiv)
Artificial intelligence (AI), especially deep learning, requires vast amounts of data for training, testing, and validation. Collecting these data and the corresponding annotations requires the implementation of imaging biobanks that provide access to these data in a standardized way. This requires careful design and implementation based on the current standards and guidelines and complying with the current legal restrictions. However, the realization of proper imaging data collections is not sufficient to train, validate and deploy AI as resource demands are high and require a careful hybrid implementation of AI pipelines both on-premise and in the cloud. This chapter aims to help the reader when technical considerations have to be made about the AI environment by providing a technical background of different concepts and implementation aspects involved in data storage, cloud usage, and AI pipelines.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 27 references that are in the map. First posted on arXiv 20 Jan 2022.
Builds on
6 of 27 references.
References · 6
- 2015Deep Learning with Limited Numerical Precisioncited by 376
- 2018Horovod: fast and easy distributed deep learning in TensorFlowcited by 279
- 2018Why Is My Classifier Discriminatory?cited by 143
- 2018A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocolscited by 49
- 2019Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Toolscited by 40
- 2020Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRIcited by 2
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Citing papers per year: 2023: 2.
2 papers.