Metadata Stores

How to track pipeline runs with metadata stores

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Keeping a historical record of your pipeline runs is a core MLOps practice. This makes it possible to trace back the lineage or provenance of the data that was used to train a model, for example, or to go back and compare the performance of a particular model at different points in time. Features like these are becoming increasingly important in production ML to help you make informed decision about the project and to provide better visibility when something goes wrong. They are especially useful in cases where legal compliance and liability are a factor.

The Metadata Store is a central component in the MLOps stack where the pipeline runtime information is versioned and stored. The configuration of each pipeline, step and produced artifacts are all tracked within the Metadata Store.

ZenML puts a lot of emphasis on guaranteed tracking of inputs across pipeline steps. Information about every pipeline run is collected and automatically recorded in the Metadata Store: the pipeline configuration, the pipeline steps and their configuration, as well as the types of artifacts produced by pipeline step runs and the location in the Artifact Store where they are kept. This is coupled with saving the artifact contents themselves in the Artifact Store to provide extremely useful features such as caching, provenance/lineage tracking and pipeline reproducibility.

Related concepts:

  • the Metadata Store is a type of Stack Component that needs to be registered as part of your ZenML Stack.

  • the Metadata Store stores information about where the artifacts produced by your pipelines are kept in the Artifact Store.

  • the Orchestrators are the stack components responsible for collecting the pipeline runtime information and storing it in the Metadata Store.

  • you can access the information recorded about your pipeline runs in the Metadata Store using the post-execution workflow API.

When to use it

The Metadata Store is a mandatory component in the ZenML stack. It is used to keep a log of detailed information about every pipeline run and you are required to configure it in all of your stacks.

Metadata Store Flavors

Out of the box, ZenML comes with a sqlite Metadata Store already part of the default stack that stores metadata in a SQLite database file on your local filesystem and a mysql Metadata Store flavor that you can connect to a MySQL compatible database. Additional Metadata Store flavors are provided by integrations. These flavors are to be used in different contexts, but in general, we suggest to use the mysql flavor for most use cases:

Metadata StoreFlavorIntegrationNotes



This is the default Metadata Store. It stores metadata information in a SQLite file on your local filesystem. Should be used only for running ZenML locally.



Connects to a MySQL compatible database service to store metadata information. Suitable and recommended for most production settings.



Kubeflow deployments include an internal metadata store, which ZenML can leverage. This flavor of Metadata Store can only to be used in combination with the Kubeflow Orchestrator.



Use this Metadata Store flavor to automatically deploy a MySQL database as a Kubernetes workload and use it as a Metadata Store backend. Not recommended for production settings.



If you would like to see the available flavors of Metadata Stores, you can use the command:

zenml metadata-store flavor list

How to use it

The Metadata Store provides low-level metadata storage services for other ZenML mechanisms. When you develop ZenML pipelines, you don't even have to be aware of its existence or interact with it directly. ZenML provides higher-level APIs that can be used as an alternative to record and access information about pipeline executions:

  • information about your pipeline step executions is automatically recorded in the Metadata Store.

  • use the post-execution workflow API to retrieve information about your pipeline runs from the active Metadata Store after a pipeline run is complete.

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