wo main options to access a deployed ZenML server:SaaS: With the Cloud offering you can utilize a control plane to create ZenML servers, also known as tenants. These tenants are managed and maintained by ZenML's dedicated team, alleviating the burden of server management from your end. Importantly, your data remains securely within your stack, and ZenML's role is primarily to handle tracking of metadata and server maintenance.

Self-hosted Deployment: Alternatively, you have the ability to deploy ZenML on your own self-hosted environment. This can be achieved through various methods, including using our CLI, Docker, Helm, or HuggingFace Spaces. We also offer our Pro version for self-hosted deployments so you can use our full paid feature-set while staying fully in control with an airgapped solution on your infrastructure.

Both options offer distinct advantages, allowing you to choose the deployment approach that best aligns with your organization's needs and infrastructure preferences. Whichever path you select, ZenML facilitates a seamless and efficient way to take advantage of the ZenML Server and enhance your machine learning workflows for production-level success.

Choose the most appropriate deployment strategy for you out of the following options to get started with the deployment:

Deploy with ZenML CLIDeploying ZenML on cloud using the ZenML CLI.

Deploy with DockerDeploying ZenML in a Docker container.

Deploy with HelmDeploying ZenML in a Kubernetes cluster with Helm.

Deploy using HuggingFace SpacesDeploying ZenML to Huggingface Spaces.

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Last updated 15 days ago