The second part of this series dives into monitoring and scaling MLOps infrastructure on Amazon EKS. It builds on foundational concepts established earlier, highlighting the specific needs of machine learning workloads compared to traditional applications. Key metrics for ML systems include model accuracy, inference latency, and resource usage. Different roles, from data scientists to DevOps engineers, require tailored monitoring strategies to ensure effective observability. ML workloads present unique challenges, given their complex pipelines involving data preprocessing, training, validation, and inference. These systems often rely on specialized hardware like NVIDIA GPUs, which excel in parallel processing necessary for deep learning tasks. The article details the advanced capabilities of modern NVIDIA GPUs, such as the Blackwell Architecture and H200 Tensor Core GPU, emphasizing their high memory bandwidth and tailored tensor cores that significantly improve performance for ML tasks. To create an effective monitoring strategy, the article suggests layering metrics according to hardware type. For instance, tracking usage percentages and memory patterns is essential for all accelerators, while NVIDIA-specific metrics like CUDA core usage and tensor core activity provide deeper insights into GPU performance. The article also introduces AWS Neuron metrics for optimized inference workloads, highlighting the importance of monitoring model execution latency and resource usage. Implementing tools like Prometheus can help collect these metrics effectively, ensuring that organizations maintain a robust MLOps infrastructure.