🤔Deploying ZenML

Why do we need to deploy ZenML?

Moving your ZenML Server to a production environment offers several benefits over staying local:

Scalability: Production environments are designed to handle large-scale workloads, allowing your models to process more data and deliver faster results.

Reliability: Production-grade infrastructure ensures high availability and fault tolerance, minimizing downtime and ensuring consistent performance.

Collaboration: A shared production environment enables seamless collaboration between team members, making it easier to iterate on models and share insights.

Despite these advantages, transitioning to production can be challenging due to the complexities involved in setting up the needed infrastructure.

ZenML Server

When you first get started with ZenML, it relies with the following architecture on your machine.

The SQLite database that you can see in this diagram is used to store information about pipelines, pipeline runs, stacks, and other configurations. Users can run the zenml up command to spin up a local REST server to serve the dashboard. The diagram for this looks as follows:

In Scenario 2, the zenml up command implicitly connects the client to the server.

In order to move into production, the ZenML server needs to be deployed somewhere centrally so that the different cloud stack components can read from and write to the server. Additionally, this also allows all your team members to connect to it and share stacks and pipelines.

Deploying a ZenML Server

Deploying the ZenML Server is a crucial step towards transitioning to a production-grade environment for your machine learning projects. By setting up a deployed ZenML Server instance, you gain access to powerful features, allowing you to use stacks with remote components, centrally track progress, collaborate effectively, and achieve reproducible results.

Currently, there are two main options to access a deployed ZenML server: