Amazon SageMaker is a fully managed service designed to simplify the processes of building, training, and deploying machine learning (ML) models, making it suitable for various applications such as recommendation systems, chatbots, and predictive analytics models. The performance and resource utilization of SageMaker ML inference endpoints, as well as the jobs that include training, processing, and batch inferences, are critical for delivering accurate and efficient user experiences. Datadog offers a solution to monitor these metrics by collecting data on latency, errors, resource utilization, and invocations, which are visualized on an integrated dashboard. This monitoring allows for quick identification of issues and optimization opportunities, such as adjusting compute instance types or scaling resources. Additionally, Datadog’s SageMaker integration supports over 850 other integrations, enabling comprehensive monitoring of SageMaker metrics alongside other AWS services, to ensure optimal resource usage and model performance.