Flexibility
PRO

Organize assets into Projects

Safe, isolated environments to structure your projects and separate concerns between teams.
A screenshot of the ZenML Pro dashboard showing a pipeline run details view and a list of pipelines and runs.

Separate concerns across projects and teams

Group pipelines, artifacts, and models into a single namespace. Easily see progress within a shared scope. Ensure that team members don’t accidentally interfere with cross-team work.
Alt text: "Dashboard displaying a list of machine learning models with details on versioning, authors, and tags for insights and predictions."
An example of a ZenML pipeline code using project templates.

Resource Sharing

Easily share specific ML assets (like models or datasets) across namespaces when needed. Control which resources are private to a namespace and which are shared

Share secrets and stacks across projects

Stacks and secrets remain globally accessible across projects. Teams can share and collaborate across environments.
A diagram showing how you can connect your infrastructure using ZenML CLI and use different tools and stacks.
Gabriel Martin
ZenML allows you to keep your ML pipeline code cloud-agnostic, enabling faster future migrations to another technology stack. The management of the metadata and artifacts generated at each step is seamless, and allows the user to extend the framework if needed without much effort.
Gabriel Martin
Machine Learning Engineer at Frontiers
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Alt text: "Dashboard displaying a list of machine learning models with details on versioning, authors, and tags for insights and predictions."