How to Package and Distribute Machine Learning Models with MLFlow - KDnuggets

One of the fundamental activities during each stage of the ML model life cycle development is collaboration. Taking an ML model from its conception to deployment requires participation and interaction between different roles involved in constructing the model. In addition, the nature of ML model development involves experimentation, tracking of artifacts and metrics, model versions, etc., which demands an effective organization for the correct maintenance of the ML model life cycle. Fortunately, there are tools for developing and maintaining a model's life cycle, such as MLflow. In this article, we will break down MLflow, its main components, and its characteristics.