Azure Blob Storage

How to store artifacts using Azure Blob Storage

This is an older version of the ZenML documentation. To read and view the latest version please visit this up-to-date URL.

The Azure Artifact Store is an Artifact Store flavor provided with the Azure ZenML integration that uses the Azure Blob Storage managed object storage service to store ZenML artifacts in a Azure Blob Storage container.

When would you want to use it?

Running ZenML pipelines with the local Artifact Store is usually sufficient if you just want to evaluate ZenML or get started quickly without incurring the trouble and the cost of employing cloud storage services in your stack. However, the local Artifact Store becomes insufficient or unsuitable if you have more elaborate needs for your project:

if you want to share your pipeline run results with other team members or stakeholders inside or outside your organization
if you have other components in your stack that are running remotely (e.g. a Kubeflow or Kubernetes Orchestrator running in public cloud).
if you outgrow what your local machine can offer in terms of storage space and need to use some form of private or public storage service that is shared with others
if you are running pipelines at scale and need an Artifact Store that can handle the demands of production grade MLOps

In all these cases, you need an Artifact Store that is backed by a form of public cloud or self-hosted shared object storage service.

You should use the Azure Artifact Store when you decide to keep your ZenML artifacts in a shared object storage and if you have access to the Azure Blob Storage managed service. You should consider one of the other Artifact Store flavors if you don't have access to the Azure Blob Storage service.

How do you deploy it?

The Azure Artifact Store flavor is provided by the Azure ZenML integration, you need to install it on your local machine to be able to register an Azure Artifact Store and add it to your stack:

zenml integration install azure -y

The only configuration parameter mandatory for registering an Azure Artifact Store is the root path URI, which needs to point to an Azure Blog Storage container and takes the form az://container-name or abfs://container-name. Please read the Azure Blob Storage documentation on how to configure an Azure Blob Storage container.

With the URI to your Azure Blob Storage container known, registering an Azure Artifact Store can be done as follows:

# Register the Azure artifact store
zenml artifact-store register az_store -f azure --path=az://container-name

# Register and set a stack with the new artifact store
zenml stack register custom_stack -a az_store ... --set

Depending on your use-case, however, you may also need to provide additional configuration parameters pertaining to authentication to match your deployment scenario.

Configuring an Azure Artifact Store in can be a complex and error prone process, especially if you plan on using it alongside other stack components running in the Azure cloud. You might consider referring to the ZenML Cloud Guide for a more holistic approach to configuring full Azure-based stacks for ZenML.

Authentication Methods

Integrating and using an Azure Artifact Store in your pipelines is not possible without employing some form of authentication. ZenML currently provides two options for configuring Azure credentials, the recommended one being to use a Secrets Manager in your stack to store the sensitive information in a secure location.

You will need the following information to configure Azure credentials for ZenML, depending on which type of Azure credentials you want to use:

an Azure connection string
an Azure account key
the client ID, client secret and tenant ID of the Azure service principle

For more information on how to retrieve information about your Azure Storage Account and Access Key or connection string, please refer to this Azure guide.

For information on how to configure an Azure service principle, please consult the Azure documentation.

This method uses the implicit Azure authentication available in the environment where the ZenML code is running. On your local machine, this is the quickest way to configure an Azure Artifact Store. You don't need to supply credentials explicitly when you register the Azure Artifact Store, instead you have to set one of the following sets of environment variables:

to use an Azure connection string, set AZURE_STORAGE_CONNECTION_STRING to your Azure Storage Key connection string
to use an Azure account key, set AZURE_STORAGE_ACCOUNT_NAME to your account name and one of AZURE_STORAGE_ACCOUNT_KEY or AZURE_STORAGE_SAS_TOKEN to the Azure key value.
to use Azure ServicePrincipal credentials, set AZURE_STORAGE_ACCOUNT_NAME to your account name and AZURE_STORAGE_CLIENT_ID, AZURE_STORAGE_CLIENT_SECRET and AZURE_STORAGE_TENANT_ID to the

The implicit authentication method needs to be coordinated with other stack components that are highly dependent on the Artifact Store and need to interact with it directly to function. If these components are not running on your machine, they do not have access to the local environment variables and will encounter authentication failures while trying to access the Azure Artifact Store:

Orchestrators need to access the Artifact Store to manage pipeline artifacts
Step Operators need to access the Artifact Store to manage step level artifacts
Model Deployers need to access the Artifact Store to load served models

These remote stack components can still use the implicit authentication method: if they are also running within the Azure Kubernetes Service, you can configure your cluster to use Azure Managed Identities. This mechanism allows Azure workloads like AKS pods to access other Azure services without requiring explicit credentials.

If you have remote stack components that are not running in AKS, or if you are unsure how to configure them to use Managed Identities, you should use one of the other authentication methods.

This method requires using a Secrets Manager in your stack to store the sensitive Azure authentication information in a secure location and configuring the Azure credentials using a ZenML secret.

The ZenML Azure secret schema supports a variety of credentials:

use an Azure account name and access key or SAS token (the account_name, account_key and sas_token attributes need to be configured in the ZenML secret)
use an Azure connection string (configure the connection_string attribute in the ZenML secret)
use Azure ServicePrincipal credentials (which requires account_name, tenant_id, client_id and client_secret to be configured in the ZenML secret)

The Azure credentials are configured as a ZenML secret that is referenced in the Artifact Store configuration, e.g.:

# Register the Azure artifact store
zenml artifact-store register az_store -f azure \
    --path='az://your-container' \
    --authentication_secret=az_secret

# Register a secrets manager
zenml secrets-manager register secrets_manager \
    --flavor=<FLAVOR_OF_YOUR_CHOICE> ...

# Register and set a stack with the new artifact store and secrets manager
zenml stack register custom_stack -a az_store -x secrets_manager ... --set

# Create the secret referenced in the artifact store
zenml secrets-manager secret register az_secret -s azure \
    --connection_sting='your-Azure-connection-string'

For more, up-to-date information on the Azure Artifact Store implementation and its configuration, you can have a look at the API docs.

How do you use it?

Aside from the fact that the artifacts are stored in Azure Blob Storage, using the Azure Artifact Store is no different than using any other flavor of Artifact Store.

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Last updated 6 months ago