Visualizations

Learn how to visualize the data artifacts produced by your ZenML pipelines.

Data visualization is a powerful tool for understanding your ML pipeline outputs. ZenML provides built-in capabilities to visualize artifacts, helping you gain insights into your data, model performance, and pipeline execution.

Accessing Visualizations

ZenML automatically generates visualizations for many common data types, making it easy to inspect your artifacts without additional code.

Dashboard Visualizations

The ZenML dashboard displays visualizations for artifacts produced by your pipeline runs:

To view visualizations in the dashboard:

1. Navigate to the Runs tab

2. Select a specific pipeline run

3. Click on any step to view its outputs

4. Select an artifact to view its visualizations

ZenML Artifact Visualizations

Notebook Visualizations

You can also display artifact visualizations in Jupyter notebooks using the visualize() method:

from zenml.client import Client

# Get an artifact from a previous pipeline run
run = Client().get_pipeline_run("<PIPELINE_RUN_ID>")
artifact = run.steps["<STEP_NAME>"].outputs[<OUTPUT_NAME>][0]

# Display the visualization
artifact.visualize()

output.visualize() Output

Supported Visualization Types

ZenML supports visualizations for many common data types out of the box:

• A statistical representation of a Pandas Dataframe represented as a png image.

• Drift detection reports by Evidently, Great Expectations, and whylogs.

• A Hugging Face datasets viewer embedded as a HTML iframe.

output.visualize() output for the Hugging Face datasets viewer

Creating Custom Visualizations

It is simple to associate a custom visualization with an artifact in ZenML, if the visualization is one of the supported visualization types. Currently, the following visualization types are supported:

• HTML: Embedded HTML visualizations such as data validation reports,

• Image: Visualizations of image data such as Pillow images (e.g. PIL.Image) or certain numeric numpy arrays,

• CSV: Tables, such as the pandas DataFrame .describe() output,

• Markdown: Markdown strings or pages.

• JSON: JSON strings or objects.

There are three ways how you can add custom visualizations to the dashboard:

• If you are already handling HTML, Markdown, CSV or JSON data in one of your steps, you can have them visualized in just a few lines of code by casting them to a special class inside your step.

• If you want to automatically extract visualizations for all artifacts of a certain data type, you can define type-specific visualization logic by building a custom materializer.

Curated Visualizations Across Resources

Curated visualizations let you surface a specific artifact visualization across multiple ZenML resources. Each curated visualization links to exactly one resource—for example, a model performance report that appears on the model detail page, or a deployment health dashboard that shows up in the deployment view.

Curated visualizations currently support the following resources:

• Projects – high-level dashboards and KPIs that summarize the state of a project.

• Deployments – monitoring pages for deployed pipelines.

• Models – evaluation dashboards and health views for registered models.

• Pipelines – reusable visual documentation attached to pipeline definitions.

• Pipeline Runs – detailed diagnostics for specific executions.

• Pipeline Snapshots – configuration/version comparisons for snapshot history.

You can create a curated visualization programmatically by linking an artifact visualization to a single resource. Provide the resource identifier and resource type directly when creating the visualization. The example below shows how to create separate visualizations for different resource types:

from uuid import UUID

from zenml.client import Client
from zenml.enums import (
    CuratedVisualizationSize,
    VisualizationResourceTypes,
)

client = Client()

# Define the identifiers for the pipeline and run you want to enrich
pipeline_id = UUID("<PIPELINE_ID>")
pipeline_run_id = UUID("<PIPELINE_RUN_ID>")

# Retrieve the artifact version produced by the evaluation step
pipeline_run = client.get_pipeline_run(pipeline_run_id)
artifact_version_id = pipeline_run.output.get("evaluation_report")
artifact_version = client.get_artifact_version(artifact_version_id)
artifact_visualizations = artifact_version.visualizations or []

# Fetch the resources we want to enrich
model = client.list_models().items[0]
model_id = model.id

deployment = client.list_deployments().items[0]
deployment_id = deployment.id

project_id = client.active_project.id

pipeline_model = client.get_pipeline(pipeline_id)
pipeline_id = pipeline_model.id

pipeline_snapshot = pipeline_run.snapshot()
snapshot_id = pipeline_snapshot.id

pipeline_run_id = pipeline_run.id

# Create curated visualizations for each supported resource type
client.create_curated_visualization(
    artifact_visualization_id=artifact_visualizations[0].id,
    resource_id=model_id,
    resource_type=VisualizationResourceTypes.MODEL,
    project_id=project_id,
    display_name="Latest Model Evaluation",
)

client.create_curated_visualization(
    artifact_visualization_id=artifact_visualizations[1].id,
    resource_id=deployment_id,
    resource_type=VisualizationResourceTypes.DEPLOYMENT,
    project_id=project_id,
    display_name="Deployment Health Dashboard",
)

client.create_curated_visualization(
    artifact_visualization_id=artifact_visualizations[2].id,
    resource_id=project_id,
    resource_type=VisualizationResourceTypes.PROJECT,
    display_name="Project Overview",
)

client.create_curated_visualization(
    artifact_visualization_id=artifact_visualizations[3].id,
    resource_id=pipeline_id,
    resource_type=VisualizationResourceTypes.PIPELINE,
    project_id=project_id,
    display_name="Pipeline Summary",
)

client.create_curated_visualization(
    artifact_visualization_id=artifact_visualizations[4].id,
    resource_id=pipeline_run_id,
    resource_type=VisualizationResourceTypes.PIPELINE_RUN,
    project_id=project_id,
    display_name="Run Results",
)

client.create_curated_visualization(
    artifact_visualization_id=artifact_visualizations[5].id,
    resource_id=snapshot_id,
    resource_type=VisualizationResourceTypes.PIPELINE_SNAPSHOT,
    project_id=project_id,
    display_name="Snapshot Metrics",
)

After creation, the returned response includes the visualization ID. You can retrieve a specific visualization later with Client.get_curated_visualization:

retrieved = client.get_curated_visualization(pipeline_viz.id, hydrate=True)
print(retrieved.display_name)
print(retrieved.resource.type)
print(retrieved.resource.id)

Curated visualizations are tied to their parent resources and automatically surface in the ZenML dashboard wherever those resources appear, so keep track of the IDs returned by create_curated_visualization if you need to reference them later.

Updating curated visualizations

Once you've created a curated visualization, you can update its display name, order, or tile size using Client.update_curated_visualization:

from uuid import UUID

client.update_curated_visualization(
    visualization_id=UUID("<CURATED_VISUALIZATION_ID>"),
    display_name="Updated Dashboard Title",
    display_order=10,
    layout_size=CuratedVisualizationSize.HALF_WIDTH,
)

When a visualization is no longer relevant, you can remove it entirely:

client.delete_curated_visualization(visualization_id=UUID("<CURATED_VISUALIZATION_ID>"))

Controlling display order and size

The optional display_order field determines how visualizations are sorted when displayed. Visualizations with lower order values appear first, while those with None (the default) appear at the end in creation order.

When setting display orders, consider leaving gaps between values (e.g., 10, 20, 30 instead of 1, 2, 3) to make it easier to insert new visualizations later without renumbering everything:

# Leave gaps for future insertions
visualization_a = client.create_curated_visualization(
    artifact_visualization_id=artifact_visualizations[0].id,
    resource_type=VisualizationResourceTypes.PIPELINE,
    resource_id=pipeline_id,
    display_name="Model performance at a glance",
    display_order=10,  # Primary dashboard
    layout_size=CuratedVisualizationSize.HALF_WIDTH,
)

visualization_b = client.create_curated_visualization(
    artifact_visualization_id=artifact_visualizations[1].id,
    resource_type=VisualizationResourceTypes.PIPELINE,
    resource_id=pipeline_id,
    display_name="Drill-down metrics",
    display_order=20,  # Secondary metrics
    layout_size=CuratedVisualizationSize.HALF_WIDTH,  # Compact chart beside the primary tile
)

# Later, easily insert between them
visualization_c = client.create_curated_visualization(
    artifact_visualization_id=artifact_visualizations[2].id,
    resource_type=VisualizationResourceTypes.PIPELINE,
    resource_id=pipeline_id,
    display_name="Raw output preview",
    display_order=15,  # Now appears between A and B
    layout_size=CuratedVisualizationSize.FULL_WIDTH,
)

RBAC visibility

Curated visualizations respect the access permissions of the resource they're linked to. A user can only see a curated visualization if they have read access to the specific resource it targets. If a user lacks permission for the linked resource, the visualization will be hidden from their view.

For example, if you create a visualization linked to a specific deployment, only users with read access to that deployment will see the visualization. If you need the same visualization to appear in different contexts with different access controls (e.g., on both a project page and a deployment page), create separate curated visualizations for each resource. This ensures that visualizations never inadvertently expose information from resources a user shouldn't access, while giving you fine-grained control over visibility.

Visualization via Special Return Types

If you already have HTML, Markdown, CSV or JSON data available as a string inside your step, you can simply cast them to one of the following types and return them from your step:

• zenml.types.HTMLString for strings in HTML format, e.g., "<h1>Header</h1>Some text",

• zenml.types.MarkdownString for strings in Markdown format, e.g., "# Header\nSome text",

• zenml.types.CSVString for strings in CSV format, e.g., "a,b,c\n1,2,3".

• zenml.types.JSONString for strings in JSON format, e.g., {"key": "value"}.

Example:

from zenml import step
from zenml.types import CSVString

@step
def my_step() -> CSVString:
    some_csv = "a,b,c\n1,2,3"
    return CSVString(some_csv)

This would create the following visualization in the dashboard:

CSV Visualization Example

Shared CSS for Consistent Visualizations

When creating multiple HTML visualizations across your pipeline, consider using a shared CSS file to maintain consistent styling. Create a central CSS file with your design system (colors, components, layouts) and Python utilities to load it into your HTML templates. This approach eliminates code duplication, ensures visual consistency across all reports, and makes it easy to update styling across all visualizations from a single location.

You can create helper functions that return complete HTML templates with shared styles, and use CSS variables for theme management. This pattern is especially valuable for teams generating multiple HTML reports or dashboards where maintaining a professional, cohesive appearance is important.

Another example is visualizing a matplotlib plot by embedding the image in an HTML string:

import matplotlib.pyplot as plt
import base64
import io

from zenml.types import HTMLString
from zenml import step, pipeline

@step
def create_matplotlib_visualization() -> HTMLString:
    """Creates a matplotlib visualization and returns it as embedded HTML."""
    # Create plot
    fig, ax = plt.subplots()
    ax.plot([1, 2, 3, 4], [1, 4, 2, 3])
    ax.set_title('Sample Plot')
    
    # Convert plot to base64 string
    buf = io.BytesIO()
    fig.savefig(buf, format='png', bbox_inches='tight', dpi=300)
    plt.close(fig)  # Clean up
    image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
    
    # Create HTML with embedded image
    html = f'''
    <div style="text-align: center;">
        <img src="data:image/png;base64,{image_base64}" 
             style="max-width: 100%; height: auto;">
    </div>
    '''
    
    return HTMLString(html)

@pipeline
def visualization_pipeline():
    create_matplotlib_visualization()

if __name__ == "__main__":
    visualization_pipeline()

Visualization via Materializers

If you want to automatically extract visualizations for all artifacts of a certain data type, you can do so by overriding the save_visualizations() method of the corresponding materializer. Let's look at an example of how to visualize matplotlib figures in your ZenML dashboard:

Example: Matplotlib Figure Visualization

1. Custom Class First, we create a custom class to hold our matplotlib figure:

from typing import Any
from pydantic import BaseModel

class MatplotlibVisualization(BaseModel):
    """Custom class to hold matplotlib figures."""
    figure: Any  # This will hold the matplotlib figure

2. Materializer Next, we create a custom materializer that handles this class and implements the visualization logic:

import os
from typing import Dict
from zenml.materializers.base_materializer import BaseMaterializer
from zenml.enums import VisualizationType
from zenml.io import fileio

class MatplotlibMaterializer(BaseMaterializer):
    """Materializer that handles matplotlib figures."""
    ASSOCIATED_TYPES = (MatplotlibVisualization,)

    def save_visualizations(
        self, data: MatplotlibVisualization
    ) -> Dict[str, VisualizationType]:
        """Create and save visualizations for the matplotlib figure."""
        visualization_path = os.path.join(self.uri, "visualization.png")
        with fileio.open(visualization_path, 'wb') as f:
            data.figure.savefig(f, format='png', bbox_inches='tight')
        return {visualization_path: VisualizationType.IMAGE}

3. Step Finally, we create a step that returns our custom type:

import matplotlib.pyplot as plt
from zenml import step

@step
def create_matplotlib_visualization() -> MatplotlibVisualization:
    """Creates a matplotlib visualization."""
    fig, ax = plt.subplots()
    ax.plot([1, 2, 3, 4], [1, 4, 2, 3])
    ax.set_title('Sample Plot')
    return MatplotlibVisualization(figure=fig)

When you use this step in your pipeline:

1. The step creates and returns a MatplotlibVisualization

2. ZenML finds the MatplotlibMaterializer and calls save_visualizations()

3. The figure is saved as a PNG file in your artifact store

4. The dashboard loads and displays this PNG when you view the artifact

For another example, see our Hugging Face datasets materializer which visualizes datasets by embedding their preview viewer.

Controlling Visualizations

Access to Visualizations

In order for the visualizations to show up on the dashboard, the following must be true:

Configuring a Service Connector

Visualizations are usually stored alongside the artifact, in the artifact store. Therefore, if a user would like to see the visualization displayed on the ZenML dashboard, they must give access to the server to connect to the artifact store.

The service connector documentation goes deeper into the concept of service connectors and how they can be configured to give the server permission to access the artifact store. For a concrete example, see the AWS S3 artifact store documentation.

When using the default/local artifact store with a deployed ZenML, the server naturally does not have access to your local files. In this case, the visualizations are also not displayed on the dashboard.

Please use a service connector enabled and remote artifact store alongside a deployed ZenML to view visualizations.

Configuring Artifact Stores

If all visualizations of a certain pipeline run are not showing up in the dashboard, it might be that your ZenML server does not have the required dependencies or permissions to access that artifact store. See the custom artifact store docs page for more information.

Enabling/Disabling Visualizations

You can control whether visualizations are generated at the pipeline or step level:

# Disable visualizations for a pipeline
@pipeline(enable_artifact_visualization=False)
def my_pipeline():
    ...

# Disable visualizations for a step
@step(enable_artifact_visualization=False)
def my_step():
    ...

You can also configure this in YAML:

enable_artifact_visualization: False

steps:
  my_step:
    enable_artifact_visualization: True

Conclusion

Visualizing artifacts is a powerful way to gain insights from your ML pipelines. ZenML's built-in visualization capabilities make it easy to understand your data and model outputs, identify issues, and communicate results.

By leveraging these visualization tools, you can better understand your ML workflows, debug problems more effectively, and make more informed decisions about your models.