AI技术考虑：数据存储、云使用和AI管道
AI Technical Considerations: Data Storage, Cloud usage and AI Pipeline
摘要：
人工智能（AI），特别是深度学习，需要大量的数据来进行训练、测试和验证。收集这些数据和相应的注释需要实施成像生物库，以标准化的方式提供对这些数据的访问。这需要根据现行标准和准则并遵守现行法律限制，认真设计和实施。然而，实现适当的成像数据汇集不足以训练、验证和部署AI，因为资源需求很高，需要在内部和云中仔细混合实施AI管道。本章旨在通过提供数据存储、云使用和AI管道中涉及的不同概念和实现方面的技术背景，帮助读者在必须对AI环境进行技术考虑时提供帮助。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
