Granular access controls

Implement fine-grained permissions based on roles and responsibilities.

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Dashboard displaying iris_logistic_regression model versioning with deployment status, highlighting machine learning and model monitoring.

Custom role definition

Create and manage roles tailored to your organization's needs

Dashboard showing MLOps tasks like training on AWS and model deployment with user roles.

Illustration of a person meditating, surrounded by logos of MLOps tools like ZenML and Kubeflow, symbolizing harmony in machine learning operations.

Integration with existing identity providers

Seamlessly connect with your current authentication systems

Audit logging (coming soon)

Track all access and permission changes for compliance and security purposes.

Data Scientist and ML Engineer collaborate using ZenML pipelines with Docker for efficient machine learning model deployment.

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

Unify Your ML and LLM Workflows

Free, powerful MLOps open source foundation

Works with any infrastructure

Upgrade to managed Pro features

Use Open Source

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