MLflow is an open-source tool for machine-learning experiment tracking and lifecycle management. The workflow to develop and continuously evolve a machine-learning model includes a series of experiments (a collection of runs), tracking the performance of these experiments (a collection of metrics) and tracking and tweaking models (projects). MLflow facilitates this workflow nicely by supporting existing open standards and integrates well with many other tools in the ecosystem. MLflow as a managed service by Databricks on the cloud, available in AWS and Azure, is rapidly maturing, and we've used it successfully in our projects. We find MLflow a great tool for model management and tracking, supporting both UI-based and API-based interaction models. Our only growing concern is that MLflow is attempting to deliver too many conflating concerns as a single platform, such as model serving and scoring.