A learning rate schedule is a crucial technique in training machine learning and deep learning models, designed to optimize the learning rate over time. The learning rate determines how quickly a model updates its parameters based on the gradients computed during training. If set too high, the model may overshoot optimal values, while a low learning rate can lead to slow convergence or getting stuck in suboptimal solutions. By employing a learning rate schedule, practitioners can dynamically adjust the learning rate, enhancing model performance and training efficiency throughout the optimization process. Various strategies, such as warmup and cosine schedules, are commonly used.