ML Repositories play a crucial role in managing, organizing, and tracking machine learning experiments and models. In this guide, we'll explore the key concepts and entities that make up ML Repositories within MLFoundry.

Concepts

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

• ML-Repo : An ML Repository is a collection of runs, models and artifacts which represents a high-level Machine Learning use-case. All access controls can be configured on the level of ml-repo. You can think of them as equivalent to Git repos - except for Machine learning artifacts.
• Runs : 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. Metrics, and parameters (details below) are all logged under a specific run.
• Models: Model comprises of model file and some metadata. Each Model can have multiple versions.
• Artifacts: Artifact is a collection of files. Each Artifact can have multiple versions.

With each run user can log metadata with the help of following :

• Parameters: Parameters or HyperParameters that define your experiment and Machine Learning model. For example, learning_rate, cache_size.
• Metric: Metrics are values that help you to evaluate and compare different runs. For example, accuracy, f1 score.
• Tags: Tags are labels for a run. A tag is represented by a string tag name and value. For example, env.