Vault-secured Credential Storage for Machine Learning Pipelines

Question

Pulumi · Accepted Answer

In the context of machine learning pipelines, securely storing and managing credentials are crucial for accessing resources like databases, storage accounts, and APIs without hard-coding sensitive information into your codebase. To securely manage these credentials, we can use a combination of Vault for secret storage and orchestration platforms like Azure Machine Learning. Vault is an open-source tool designed for securing, storing, and tightly controlling access to tokens, passwords, certificates, API keys, and other secrets.

Below, I'll guide you through setting up a Vault-secured credential storage and integration with an Azure Machine Learning pipeline using Pulumi in Python.

Prerequisites

For the following program to work, you should have the following set up:

Vault server up and running with the necessary permissions.
Azure account with the necessary permissions to create and manage Azure Machine Learning resources.
Pulumi CLI and Python set up on your machine.
Appropriate configuration for Pulumi to connect to your Azure account.

Setting up Vault-Secured Credential Storage

We will use Pulumi to create an Azure Machine Learning Workspace and configure Vault to securely store a secret. For the sake of simplicity, we won't deploy an actual machine learning pipeline but will set up the foundational infrastructure where Vault is leveraged to store the credentials used by Machine Learning services.

Let's start by creating a Pulumi program that provisions a Vault secret and an Azure Machine Learning workspace:

import pulumi
import pulumi_vault as vault
import pulumi_azure_native.machinelearningservices as machinelearningservices

# Instantiate an Azure Machine Learning workspace
# Replace '<location>' with the desired Azure location for your resources.
ml_workspace_name = "my-ml-workspace"
ml_workspace = machinelearningservices.Workspace("mlWorkspace",
    resource_group_name="<resource_group_name>",
    location="<location>",
    workspace_name=ml_workspace_name,
    sku="Basic",  # Sku can also be "Enterprise" depending on your needs
)

# Vault Secret Backend Configuration
# Replace these with the actual paths and token where your Vault server is running.
vault_address = "http://your-vault-server:8200"
vault_token = "your-vault-token"

# Authenticate to Vault
vault_provider = vault.Provider("vault",
    address=vault_address,
    token=vault_token,
)

# Securely store an api_key in Vault
api_key_secret = vault.GenericSecret("apiKeySecret",
    path="secret/data/ml_api_key",
    data_json="""{
        "api_key": "super-secret-api-key"
    }""",
    opts=pulumi.ResourceOptions(provider=vault_provider),
)

pulumi.export("ml_workspace_name", ml_workspace.name)
pulumi.export("api_key_secret_id", api_key_secret.id)

Explanation

Azure Machine Learning Workspace: This is a foundational service in Azure for machine learning that helps you manage and organize your machine learning efforts. A workspace holds all your models, experiments, and deployments. The machinelearningservices.Workspace resource creates a new workspace.
Vault Provider: The vault.Provider is used to configure how we connect to our Vault server. It includes the address of the Vault server and a token used to authenticate.
Vault Generic Secret: The vault.GenericSecret resource is used to store the api_key in Vault. This secret can be used by your machine learning services to authenticate to various services without embedding the raw API key in your code.
Exports: The pulumi.export statements output the name of the workspace and the ID of the stored API key for easy access and reference.

You can now run this Pulumi program which will provision the necessary resources and store your API key securely in Vault. Your machine learning pipeline can be adjusted to retrieve the API key from Vault, and use it to authenticate to whatever services are necessary.

Remember that for this code to work, you need to replace placeholders like <resource_group_name>, <location>, and Vault server credentials with actual values. Also, make sure your Pulumi and Vault CLI is configured to connect to your Azure and Vault environments respectively.