This Guidance provides best practices to help you optimize machine learning (ML) operations (MLOps) for environmental sustainability. While customers across industries are committed to reducing their carbon footprints, ML workloads are becoming increasingly complex and consuming more energy and resources. This Guidance helps you review and refine your workloads to maximize utilization and minimize waste and the total resources deployed and powered to support your workload at all aspects of the ML lifecycle, including data collection, data storage, feature engineering, training, inference, and deployment.