AI（人工智能）的行程方式是怎样的？
人工智能（AI）的运行方式因应用场景和任务而异，随着技术的不断发展，AI（人工智能）的运行方式也在不断发生变化。以下是一些常见的AI（人工智能）运行方式。
1. 在云端运行：在云端运行是一种常见的AI（人工智能）运行方式。在这种场景下，AI（人工智能）系统的计算和存储资源位于云端服务器上，客户端和其他终端通过互联网连接到云端以访问计算和存储资源。这种模式消除了客户需求较高的硬件和软件设施，并可以确保数据的安全存储。
2. 在设备端运行：在设备端运行是AI（人工智能）系统运行的另一种模式，也被称为本地运行。在这种模式下，AI（人工智能）系统在设备（例如智能手机、智能音箱等）上运行，而不是通过云服务。这种模式可以使AI（人工智能）系统更具适应性，并支持实时响应和互动。
3. 在边缘设备上运行：在边缘设备上运行指AI（人工智能）系统在网络接近终端设备的位置上执行。它比云端或设备本地运行提供了更快的响应时间，并可以支持实时的数据流和分析。边缘设备可以是传感器、路由器、基站和智能家居等物联网设备。 数字化转型网www.szhzxw.cn
4. 在混合云环境中运行：混合云环境是指将云计算和本地IT资源集成在一个统一的体系结构中，AI（人工智能）系统可以在其中运行。在这种模式下，AI（人工智能）系统可以在多个云和本地计算机上进行分布式处理，实现高可用性、高性能、更好的安全性和更好的维护性。
总体来讲，AI（人工智能）系统可以在云端、本地、边缘设备或混合云环境中运行。每种运行方式都有自己的优缺点，并且适合不同的应用场景和任务。作为AI（人工智能）技术的用户和开发者，需要了解不同运行模式的特点和适用范围，以便为商业决策做出更明智的选择。
在人工智能（AI）应用中，数据是非常重要的资源，它是建立机器学习模型的基础。以下是关于数据种类和数据来源的概述。 数字化转型网www.szhzxw.cn
数字化转型网人工智能专题
与全球关注人工智能的顶尖精英一起学习！数字化转型网建立了一个专门讨论人工智能技术、产业、学术的研究学习社区，与各位研习社同学一起成长！欢迎扫码加入！ 数字化转型网www.szhzxw.cn
本文由数字化转型网（www.szhzxw.cn）转载而成，来源于网络；编辑/翻译：数字化转型网宁檬树。
翻译：
What is the way AI (artificial intelligence) travels?
The way artificial intelligence (AI) operates varies by application scenario and task, and the way AI (artificial intelligence) operates is constantly changing as the technology continues to evolve. Here are some common ways AI (artificial intelligence) works.
1. Run in the cloud: Running in the cloud is a common way of running AI (artificial intelligence). In this scenario, the computing and storage resources of the AI (artificial intelligence) system are located on cloud servers, and clients and other terminals are connected to the cloud via the Internet to access computing and storage resources. This model eliminates hardware and software facilities with high customer demand and ensures secure storage of data.
2. Run on the device side: Run on the device side is another mode of AI (artificial intelligence) system operation, also known as local operation. In this model, AI (artificial intelligence) systems run on devices (such as smartphones, smart speakers, etc.) rather than via cloud services. This pattern can make AI (artificial intelligence) systems more adaptable and support real-time response and interaction.
3. Run on edge devices: Run on edge devices refers to the execution of AI (artificial intelligence) systems at the position of the network close to the end device. It provides faster response times than cloud or on-premises operations and can support real-time data flow and analytics. Edge devices can be iot devices such as sensors, routers, base stations and smart homes.
4. Run in a hybrid cloud environment: A hybrid cloud environment refers to the integration of cloud computing and on-premises IT resources in a unified architecture in which AI (artificial intelligence) systems can run. In this model, AI (artificial intelligence) systems can perform distributed processing across multiple clouds and on-premises computers, achieving high availability, high performance, better security, and better maintenance. 数字化转型网www.szhzxw.cn
In general, AI (artificial intelligence) systems can run in cloud, on-premises, edge devices, or hybrid cloud environments. Each mode of operation has its own advantages and disadvantages, and is suitable for different application scenarios and tasks. As users and developers of AI (artificial intelligence) technology, it is necessary to understand the characteristics and scope of different operating modes in order to make more informed choices for business decisions.
In artificial intelligence (AI) applications, data is a very important resource, and it is the basis for building machine learning models. Below is an overview of the types and sources of data.
