A run represents a single execution of your code.
Azure ML is a machine-learning service that facilitates running your code in
the cloud. A
Run is an abstraction layer around each such submission, and is used to
monitor the job in real time as well as keep a history of your results.
An experiment is a light-weight container for
Run. Use experiments to submit
and track runs.
Create an experiment in your workspace
Usually a run is created by submitting a ScriptRunConfig.
For more details: ScriptRunConfig
Code that is running within Azure ML is associated to a
Run. The submitted code
can access its own run.
A common use-case is logging metrics in a training script.
When this code is submitted to Azure ML (e.g. via ScriptRunConfig) it will log metrics to its assocaited run.
For more details: Logging Metrics
In an interactive setting e.g. a Jupyter notebook
A common use case for interacive logging is to train a model in a notebook.
Follow the link to the run to see the metric logging in real time.