Distributed AI
Distributed AI is a paradigm in artificial intelligence where computational tasks, data, or models are distributed across multiple nodes or systems to improve scalability, efficiency, and performance. It involves techniques like parallel processing, federated learning, and distributed training to handle large-scale AI workloads that exceed the capabilities of single machines. This approach is essential for training complex models on massive datasets, enabling real-time inference, and leveraging decentralized resources.
Developers should learn Distributed AI when working on large-scale machine learning projects, such as training deep neural networks on terabytes of data, deploying AI in edge computing environments, or ensuring privacy in sensitive applications. It is crucial for use cases like autonomous vehicles, recommendation systems, and healthcare analytics, where data is inherently distributed or computational demands are high. Mastering this concept helps optimize resource usage, reduce training times, and build robust, scalable AI systems.