📈Experiment Trackers

Logging and visualizing ML experiments.

Experiment trackers let you track your ML experiments by logging extended information about your models, datasets, metrics, and other parameters and allowing you to browse them, visualize them and compare them between runs. In the ZenML world, every pipeline run is considered an experiment, and ZenML facilitates the storage of experiment results through Experiment Tracker stack components. This establishes a clear link between pipeline runs and experiments.

Related concepts:

the Experiment Tracker is an optional type of Stack Component that needs to be registered as part of your ZenML Stack.

ZenML already provides versioning and tracking for the pipeline artifacts by storing artifacts in the Artifact Store.

When to use it

ZenML already records information about the artifacts circulated through your pipelines by means of the mandatory Artifact Store.

However, these ZenML mechanisms are meant to be used programmatically and can be more difficult to work with without a visual interface.

Experiment Trackers on the other hand are tools designed with usability in mind. They include extensive UIs providing users with an interactive and intuitive interface that allows them to browse and visualize the information logged during the ML pipeline runs.

You should add an Experiment Tracker to your ZenML stack and use it when you want to augment ZenML with the visual features provided by experiment tracking tools.

How they experiment trackers slot into the stack

Here is an architecture diagram that shows how experiment trackers fit into the overall story of a remote stack.

Experiment Tracker Flavors

Experiment Trackers are optional stack components provided by integrations: