Accelerated AI and ML Workloads refer to the use of specialized hardware accelerators, such as GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units), to enhance the performance of artificial intelligence (AI) and machine learning (ML) tasks.

These accelerators are designed to handle the computational demands of training and inference processes more efficiently than traditional CPUs, leading to faster execution times and improved overall performance of AI and ML algorithms. This acceleration allows for quicker model training, more rapid inference, and the ability to tackle larger and more complex datasets, thereby advancing the capabilities of AI and ML applications across various domains.