Mixture of Experts (MoE) is a machine learning framework that employs multiple specialized sub-models, or "experts," to handle different aspects of a task, guided by a "gating network" that assigns weights to each expert's output. Unlike traditional dense models that engage all parameters for every input, MoE selectively activates relevant experts, resulting in reduced computational costs and improved scalability without compromising capacity. MoE is particularly beneficial for large language models, offering advantages like reduced inference latency, enhanced training scalability, and improved modularity and interpretability. The guide provides a detailed tutorial on implementing an MoE system using Python, showcasing the process through a practical example where news articles are summarized and analyzed for sentiment using distinct expert models. This approach highlights the efficiency and flexibility of MoE in handling diverse data types and tasks, with the potential for more nuanced and effective processing compared to monolithic dense networks.