Concepts

The entities defined in MLFoundry can be understood from the diagram below.

• Run : A run represents a single experiment which in the context of Machine Learning is one specific model (say Logistic Regression), with a fixed set of hyper-parameters. Models, artifacts, metrics, and parameters (details below) are all logged under a specific run.
• Project: Multiple runs are organized under a single project which represents a high-level business problem. Access controls and collaboration with teammates happen on a project level.
• Artifacts: An artifact can be any file or directory.
• Parameters: Change this to -> Parameters or HyperParameters that define your experiment and Machine Learning model. For example, learning_rate.
• Metric: Metrics are values that help you to evaluate and compare different runs.
• Dataset: The piece of data on which machine learning models are trained.