# AI Technical Considerations: Data Storage, Cloud usage and AI Pipeline

arXiv: https://arxiv.org/abs/2201.08356 · HTML: https://mlsanity.com/paper/2201.08356/

## 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 (most cited first, 6 of 6)

- [Deep Learning with Limited Numerical Precision](https://mlsanity.com/paper/1502.02551/) (2015)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [Why Is My Classifier Discriminatory?](https://mlsanity.com/paper/1805.12002/) (2018)
- [A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols](https://mlsanity.com/paper/1805.10170/) (2018)
- [Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Tools](https://mlsanity.com/paper/1903.11314/) (2019)
- [Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI](https://mlsanity.com/paper/2010.06027/) (2020)

## Built on by (most cited first, 2 of 2)

- [Exploring adversarial attacks in federated learning for medical imaging](https://mlsanity.com/paper/2310.06227/) (2023)
- [The Hidden Adversarial Vulnerabilities of Medical Federated Learning](https://mlsanity.com/paper/2310.13893/) (2023)

## 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.