ons. 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.Copyfrom 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:

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

Discover advanced ZenML features like config management and containerization.

Explore ZenML through practical use-case examples.

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Last updated 14 days ago