Because MLflow is an API-first platform, there are many patterns for using it in complex workflows and integrating it with existing tools. In this talk, we’ll demo a few best practices for using MLflow in a more complex workflow. These include: * Run multi-step workflows on MLflow, such as data preparation steps followed by training, and organizing your projects so you can automatically reuse past work. * Tune Hyperparameter on MLflow with open source hyperparameter tuning packages. * Save a model in MLflow (eg, from a new machine learning library) and deploying it to the existing deployment tools.