Introduction
Start here with the ZenML Docs.
ZenML is an extensible, open-source MLOps framework for creating portable, production-ready MLOps pipelines. Built for data scientists, ML Engineers, and MLOps Developers to collaborate, it has a simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows.
At its core, ZenML pipelines execute ML-specific workflows from sourcing data to splitting, preprocessing, training, all the way to serving and monitoring ML models in production. There are many built-in features to support common ML development tasks. ZenML is not here to replace the great tools that solve these individual problems. Rather, it offers an extensible framework and a standard abstraction to write and build your workflows.

Pipelining - Take your first ZenML Steps

Every journey has to start somewhere, if you are new to ZenML, you might want to get acquainted with the core concepts first, to understand what makes ZenML so special. Afterwards, you could jump right into our developer guide that will take you from zero to hero in no time and explain the necessary knowledge of how to use ZenML.

Stacking - Customize your Stack

Already ran your first pipelines and want to know about integrations and production use cases? Our Advanced Guides are the right place for you. You'll find some more detailed descriptions of specific use cases and features here. This is where you'll learn how to take your MLOps stack from basic to fully loaded.

Extending - Make it your own

ZenML does not natively support your favorite tool? Don't you worry! ZenML is built from the ground up with extensibility in mind. Find out how to integrate other tools or proprietary on-prem solutions for you and your team in our section on Stack Components.

Collaborating - Work together with your team

Share not only your code but also your ZenML stacks with your team. Find out how in our section on collaboration.

Resources - Learn ZenML with tutorials, examples, and guides

The ZenML team and community has put together many more resources other than the documentation to learn about the framework. Learn more in our section on resources.
Last modified 10d ago