MLflow

Allows to organize a experiment into runs and keep track of:

• Parameters
• Metrics
• Metadata
• Artifacts
• Models

Automatically logs extra information about the run like:

• Source code
• Version of the code (git commit)
• Start and end time
• Author

Backend store

• Local file system
• SQLAlchemy compatible database (MySQL, Postgres, SQLite, etc)

Artifact store

• Local file system
• Amazon S3
• Azure Blob Storage
• Google Cloud Storage
• HDFS

MLflow Tracking API

• No tracking server: logs to local file system
• Localhost
• Remote server