Abstract: This paper presents how VMware addressed the following challenges in operationalizing our ML-based performance diagnostics solution in enterprise hybrid-cloud environments: data governance, model serving and deployment, dealing with system performance drifts, selecting model features, centralized model training pipeline, setting the appropriate alarm threshold, and explainability. We also share the lessons and experiences we learned over the past four years in deploying ML operations at scale for enterprise customers.