Organize assets into Projects

Safe, isolated environments to structure your projects and separate concerns between teams.

ZenML dashboard displaying machine learning pipeline runs and experiment tracking with statuses and components.

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.

Dashboard displaying machine learning models, including versions, authors, and tags. Relevant to model monitoring and ML pipelines.

"ZenML pipeline diagram showcasing process flow for model training and deployment, ideal for MLOps and automated machine learning."

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.

Diagram of various machine learning tools and frameworks including ZenML, Kubeflow, Vertex AI, and AWS SageMaker for ML pipelines integration.

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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