Investigated the deployment of a deep learning model using MLFlow on Kubernetes clusters across three major cloud platforms: IBM Cloud, Google Cloud Platform (GCP), and Amazon Web Services (AWS). We analyzed the performance of the model in terms of training time, job execution time, test loss, and data loading time on each platform, using MLFlow metrics and tracking. Our findings provided insights into factors that may influence the choice of a cloud platform for MLflow deployment.