However, there are a few additional use cases that you might or might not encounter throughout your journey, about which you can learn more here.
List of Advanced Use Cases
​Writing Custom Stack Component Flavors can be useful when trying to use ZenML with tooling or infrastructure for which no official integration exists yet.
​Managing Stack Component States is required for certain integrations with remote components and can also be used to configure custom setup behavior of custom stack component flavors.
​Passing Custom Data Types through Steps via Materializers is required if one of your steps outputs a custom class or other data types, for which materialization is not defined by ZenML itself.
​Accessing the Active Stack within Steps via Step Fixtures can, for instance, be used to load the best performing prior model to compare newly trained models against.
​Accessing Global Info within Steps via the Environment can be useful to get system information, the Python version, or the name of the current step, pipeline, and run.
​Managing External Services might be required for deploying custom models or for the UI's of some visualization tools like TensorBoard. These services are usually long-lived external processes that persist beyond the execution of your pipeline runs.
​Managing Docker Images is required for some remote orchestrators and step operators to run your pipeline code in an isolated and well-defined environment.