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Azure Blob Storage
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ZenML
Efficiently Store and Share ZenML Artifacts with Azure Blob Storage
Azure Blob Storage
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Azure Blob Storage

Efficiently Store and Share ZenML Artifacts with Azure Blob Storage
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Efficiently Store and Share ZenML Artifacts with Azure Blob Storage

Enhance your ZenML workflows by leveraging Azure Blob Storage as a scalable and reliable artifact store. This integration enables seamless storage and sharing of pipeline artifacts, making it ideal for collaborative ML projects and production-grade MLOps.

Features with ZenML

  • Seamlessly store and retrieve pipeline artifacts in Azure Blob Storage
  • Enable collaboration by sharing artifacts across teams and stakeholders
  • Scale storage effortlessly to handle the growing demands of ML projects
  • Integrate with other Azure-based stack components for end-to-end MLOps
  • Secure access to artifacts using Azure authentication methods

Main Features

  • Scalable and durable object storage for unstructured data
  • High availability and geo-redundancy options
  • Flexible access control and security features
  • Cost-effective storage for large-scale ML artifacts
  • Seamless integration with other Azure services

How to use ZenML with
Azure Blob Storage

# 1. Install the ZenML `azure` integration
# zenml integration install azure

# 2. Register an Azure artifact store
# zenml artifact-store register <NAME> --flavor azure --path=<PATH_TO_STORAGE>

# 3. Register a stack with the new artifact store
# zenml register stack <STACK_NAME> -a <NAME> -o default --set

from typing import Annotated

from zenml import pipeline, step
from zenml.client import Client


@step
def hello_world() -> Annotated[str, "my_first_artifact"]:
    return "Hello World!"


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


if __name__ == "__main__":
    my_pipeline()

    # Fetch the artifact and print it
    print("Result: ", Client().get_artifact_version("my_first_artifact").load())
    

This code example demonstrates how to set up and use the Azure Blob Storage integration with ZenML:

  1. Install the Azure integration
  2. Register an Azure Artifact Store by using the path to your blob storage
  3. Register a ZenML stack with the Azure Artifact Store
  4. Use the @step and @pipeline decorators to define a pipeline to store artifacts in the Azure Artifact Store.

Additional Resources
Read the guide for setting up a full Azure stack
Read the ZenML Azure Blob Storage Artifact Store Documentation
Azure Blob Storage documentation

Efficiently Store and Share ZenML Artifacts with Azure Blob Storage

Enhance your ZenML workflows by leveraging Azure Blob Storage as a scalable and reliable artifact store. This integration enables seamless storage and sharing of pipeline artifacts, making it ideal for collaborative ML projects and production-grade MLOps.
Azure Blob Storage

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