Understanding MLflow plugins

As an ML engineer, multiple times in your project you can reach the limits of a framework. MLflow provides an extension system through its plugin features. A plugin architecture allows the extensibility and adaptability of a software system.

MLflow allows the creation of the following types of plugins:

• Tracking store plugins: This type of plugin controls and tweaks the store that you use to log your experiment metrics in a specific type of data store.
• Artifact repository: You are able to override the artifact repositories with your own storage system—for example, adding an artifact repository based on the Hadoop Distributed File System (HDFS) or any object store specific to your environment, overriding API calls such as log_artifact and download_artifacts.
• Running context providers: You can update how your system logs information about the context—for instance, tags such as git_tags and repo_uri, and other relevant elements...