"hello", input_two=output_step_one)

my_pipeline()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 https://www.zenml.io/live-demo!

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.

🚀 Learn More

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

Understand the core concepts behind ZenML.

Get started with ZenML and learn how to build your first pipeline and stack.

Build your first ZenML pipeline and deploy it in the cloud.

ZenML empowers ML engineers to take ownership of the entire ML lifecycle end-to-end. Adopting ZenML means fewer handover points and more visibility on what is happening in your organization.

ML Lifecycle Management: ZenML's abstractions enable you to manage sophisticated ML setups with ease. After you define your ML workflows as Pipelines and your development, staging, and production infrastructures as Stacks, you can move entire ML workflows to different environments in seconds.Copyzenml stack set staging
python run.py  # test your workflows on staging infrastructure
zenml stack set production
python run.py  # run your workflows in production

Reproducibility: ZenML enables you to painlessly reproduce previous results by automatically tracking and versioning all stacks, pipelines, artifacts, and source code. In the ZenML dashboard, you can get an overview of everything that has happened and drill down into detailed lineage visualizations. Try it out at https://www.zenml.io/live-demo!