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Advanced guide

Taking your ZenML workflow to the next level.

The aim of this section is to provide you with detailed explanations and documentation regarding specific cases, problems, and solutions frequently encountered in ML workflows.
Configure steps/pipelines
Configuring pipelines, steps, and stack components in ZenML.
Visualize artifacts
Configuring ZenML to display data visualizations in the dashboard.
Manage environments
Developing across multiple development environments.
Containerize your pipeline
Using Docker images to run your pipeline.
Connect your git repository
Tracking your code and avoiding unnecessary docker builds by connecting your git repo.
Leverage community-contributed plugins
Collaborating with the ZenML community.
Data versionioning and artifacts management
Managing your data with ZenML.
Schedule pipeline runs
Planning runs to add automation to your pipelines.
Fetch metadata within steps
Accessing meta information in real-time within your pipeline.
Use failure/success hooks
Running failure and success hooks after step execution.
Handle custom data types
Using materializers to pass custom data types through steps.
Hyper-parameter tuning
Running a hyper-parameter tuning trial with ZenML.
Scale compute to the cloud
Ensuring your pipelines or steps run on GPU-backed hardware.
Global settings of ZenML
Understanding the global settings of your ZenML installation.

Follow best practices

Configure steps/pipelines

Last modified 3d ago