To improve a distributed AI application that experiences delays and data loss, what action is best to enhance performance and reliability?

Implementing a dedicated, high-bandwidth network link between IoT devices and the data processing centers directly addresses the challenges of delays and data loss that a distributed AI application may experience. This approach enhances performance by ensuring that data can be transmitted quickly and reliably between devices and processing centers without being affected by the congestion or limitations of a shared network.

A high-bandwidth link offers several benefits, such as minimizing latency, which is critical for real-time data processing and decision-making in AI applications. By reducing the chances of packet loss and transmission delays, the application can achieve a smoother operation, ultimately leading to a more efficient and responsive system.

While other options may provide improvements in specific areas, they do not directly tackle the core issues of network performance and reliability as effectively as establishing a dedicated network link. For instance, switching to a batch processing model can reduce the frequency of data transfers but might not sufficiently mitigate delays that occur during those transfers or solve the inherent issues of data loss. Upgrading IoT devices can improve processing capabilities but won't necessarily resolve network-related delays or data loss during transmission. Deploying a CDN may cache data closer to users but primarily enhances content delivery rather than the reliability of direct data transmission from IoT devices to processing centers.

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