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Distributed Systems Design involves creating and implementing systems where components, including software and hardware, are located on multiple machines. These systems are designed to leverage the benefits of parallelism, reliability, and scalability by distributing tasks across various nodes. In the context of Artificial Intelligence, Distributed Systems Design plays a crucial role in building large-scale AI applications that require processing vast amounts of data and handling complex computations efficiently. By harnessing the power of multiple machines and optimizing communication between them, this subfield enables AI models to learn from extensive datasets and deliver accurate predictions quickly. As part of Data Science, Distributed Systems Design facilitates big data processing, ensuring that large-scale analytics and machine learning tasks can be carried out effectively. Furthermore, in the broader Technology landscape, Distributed Systems Design is essential for creating robust and high-performance software architectures capable of meeting modern demands for seamless user experiences and efficient data management.