Discover the two primary methods for achieving parallelism in AI: data parallelism and model parallelism. Data parallelism is highlighted as the more straightforward approach, where multiple nodes share the same model but process different data chunks. The discussion also covers the mechanics of gradient sharing, emphasizing the simplicity of the rreduce method and the role of a parameter server in coordinating updates across nodes.
