Seamlessly Orchestrate ML Pipelines on Azure with ZenML

Integrate the power of Microsoft Azure with ZenML to effortlessly orchestrate and manage your machine learning pipelines in the cloud. This integration allows you to leverage Azure's scalable infrastructure and comprehensive ML services while benefiting from ZenML's streamlined workflow management capabilities.

Features with ZenML

Seamless deployment of ZenML pipelines on Azure infrastructure
Scalable compute resources for efficient model training and inference
Enable collaboration by sharing artifacts across teams and stakeholders
Flexible orchestrator settings for serverless, compute instance or compute cluster modes
Secure access to artifacts using Azure authentication methods

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Main Features

Comprehensive cloud-based environment for the entire ML lifecycle
Scalable compute resources and managed services for ML workloads
Visual interface for monitoring and managing ML experiments and models
Seamless integration with other Azure services and tools
Enterprise-grade security and compliance features

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How to use ZenML with

Microsoft Azure


# Register a ZenML Azure stack by using existing infrastructure
# zenml stack register <STACK_NAME> -p azure

# OR, create a ZenML Azure stack by deploying new infrastructure
# zenml stack deploy -p azure

from zenml import pipeline, step


@step
def hello_world() -> str:
    return "Hello World!"


@pipeline
def my_pipeline():
    _ = hello_world()


if __name__ == "__main__":
    my_pipeline()

The code example demonstrates how to register an Azure stack with ZenML:

Use the CLI to register a stack using either by using existing infrastructure or by deploying it anew
Use the @step and @pipeline decorators to define a pipeline and run it on your new Azure stack.

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Additional Resources

Read a detailed Azure guide on how to establish a ZenML Azure stack and execute your pipelines

Deploy an Azure stack using the 1-click deployment tool