论文标题
部分可观测时空混沌系统的无模型预测
AI Technical Considerations: Data Storage, Cloud usage and AI Pipeline
论文作者
论文摘要
人工智能（AI），尤其是深度学习，需要大量的数据进行培训，测试和验证。收集这些数据和相应的注释需要实现成像生物库，以标准化的方式访问这些数据。这需要根据当前标准和准则进行仔细的设计和实施，并遵守当前的法律限制。但是，由于资源需求很高，因此实现适当的成像数据收集不足以训练，验证和部署AI，并且需要对本地和云中的AI管道进行仔细的混合实施。本章旨在通过提供有关数据存储，云用法和AI管道中涉及的不同概念和实现方面的技术背景来帮助读者对AI环境的技术考虑。
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.
