Introduction

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 run.py  # develop your code locally with all your favorite tools
zenml stack set production
python run.py  # 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

@step
def step_1() -> str:
  return "world"

@step
def step_2(input_one: str, input_two: str) -> None:
  combined_str = input_one + ' ' + input_two
  print(combined_str)

@pipeline
def my_pipeline():
  output_step_one = step_1()
  step_2(input_one="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.

🚀 Learn More

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

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.

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  zenml 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!

• Automated Deployments: With ZenML, you no longer need to upload custom Docker images to the cloud whenever you want to deploy a new model to production. Simply define your ML workflow as a ZenML pipeline, let ZenML handle the containerization, and have your model automatically deployed to a highly scalable Kubernetes deployment service like Seldon.

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  from zenml.integrations.seldon.steps import seldon_model_deployer_step
  from my_organization.steps import data_loader_step, model_trainer_step
  
  @pipeline
  def my_pipeline():
    data = data_loader_step()
    model = model_trainer_step(data)
    seldon_model_deployer_step(model)

🚀 Learn More

Ready to manage your ML lifecycles end-to-end with ZenML? Here is a collection of pages you can take a look at next: