Introduction
MLflow is an open source platform for end-to-end machine learning operations. Using MLflow, data scientists can track model training experiments; logging parameters, metrics, and other assets. Machine learning engineers can use MLflow to deploy and manage models, enabling applications to consume the models and use them to inference predictions for new data.
MLflow is natively supported in Azure Databricks. This module teaches you how to use it to track model training experiments and to register and publish models.