Welcome to ZenML!

This is an older version of the ZenML documentation. To read and view the latest version please visit this up-to-date URL.

⭐ Introduction

ZenML is an extensible, open-source MLOps framework for creating portable, production-ready machine learning pipelines. By decoupling infrastructure from code, ZenML enables developers across your organization to collaborate more effectively as they develop to production.

ZenML gives data scientists the freedom to fully focus on modeling and experimentation while writing code that is production-ready from the get-go.

  • Develop Locally: ZenML allows you to develop ML models in any environment using your favorite tools. This means you can start developing locally, and simply switch to a production environment once you are satisfied with your results.

    python  # develop your code locally with all your favorite tools
    zenml stack set production
    python  # run on production infrastructure without any code changes
  • Pythonic SDK: ZenML is designed to be as unintrusive as possible. Adding a ZenML @step or @pipeline decorator to your Python functions is enough to turn your existing code into ZenML pipelines:

    from zenml import pipeline, step
    def step_1() -> str:
      return "world"
    def step_2(input_one: str, input_two: str) -> None:
      combined_str = input_one + ' ' + input_two
    def my_pipeline():
      output_step_one = step_1()
      step_2(input_one="hello", input_two=output_step_one)
  • Automatic Metadata Tracking: ZenML automatically tracks the metadata of all your runs and saves all your datasets and models to disk and versions them. Using the ZenML dashboard, you can see detailed visualizations of all your experiments. Try it out at!

ZenML integrates seamlessly with many popular open-source tools, so you can also combine ZenML with other popular experiment tracking tools like Weights & Biases, MLflow, or Neptune for even better reproducibility.

Ready to develop production-ready code with ZenML? Here is a collection of pages you can take a look at next:

Last updated