Collaborate with ZenML
Collaboration with ZenML.
Using ZenML to develop and execute pipelines from the comfortable confines of your workstation or laptop is a great way to incorporate MLOps best practices and production grade quality into your project from day one. However, as machine learning projects grow, managing and running ZenML in a single-user and single-machine setting can become a strenuous task. More demanding projects involve creating and managing an increasing number of pipelines and complex Stacks built on a wider and continuously evolving set of technologies. This can easily exceed the capabilities of a single machine and person or role.
The same principles observed in the ZenML single-user experience are applicable as a framework of collaboration between the various specialized roles in the AI/ML team. We purposefully maintain a clean separation between the core ZenML concepts, with the intention that they may be managed as individual responsibilities assigned to different members of a team or department without incurring the overhead and friction usually associated with larger organizations.
One such example is the decoupling of ZenML Stacks from pipelines and their associated code, a principle that provides for a smooth transition from experimenting with and running pipelines locally to deploying them to production environments. It is this same decoupling that also allows ZenML to be used in a setting where part of a team is iterating on writing ML code and implementing pipelines, while the other part is defining and actively maintaining the infrastructure and the Stacks that will be used to execute pipelines in production. Everyone can remain focused on their responsibilities with ZenML acting as the central piece that connects and coordinates everything together.
The ability to configure modular Stacks in which every component has a specialized function is another way in which ZenML maintains a clean separation of concerns. This allows for a MLOps Stack design that is natively flexible and extensible that can evolve organically to match the size and structure of your AI/ML team and organization as you iterate on your project and invest more resources into its development.
This documentation section is dedicated to describing several ways in which you can deploy ZenML as a collaboration framework and enable your entire AI/ML team to enjoy its advantages.

Export and Import ZenML Stacks

If you need to quickly share your Stack configuration with someone else, there is nothing easier than using the ZenML CLI to export a Stack in the form of a YAML file and import it somewhere else.

Organize and Share with Profiles

With ZenML Profiles, you can unlock a range of strategies for organizing and managing ZenML configurations that are available across your entire team. Stacks, Stack Components and other classes of ZenML objects can be stored in a central location and shared across multiple users, teams and automated systems such as CI/CD processes.

Centralized ZenML Management with ZenServer

With the ZenServer, you can deploy ZenML as a centralized service and connect entire teams and organizations to an easy to manage collaboration platform that provides a unified view on the MLOps processes, tools and technologies that support your entire AI/ML project lifecycle.