AI Model Training Data Repositories on OSS

Question

Pulumi · Accepted Answer

To create AI model training data repositories on Object Storage Service (OSS), you would typically perform the following steps:

1. Choose a cloud provider and create an Object Storage Service (OSS) bucket where your data will reside.
2. Upload your datasets to the OSS bucket.
3. Optionally, configure access controls to ensure that your data is secure and only accessible by authorized entities.
4. Use this data repository as the source for training your machine learning models.

For the purpose of this explanation, I will guide you through setting up a data repository on Azure using Pulumi and Python.

Azure Machine Learning (Azure ML) is a cloud service you can use to track your machine learning models, and it provides a registry for storing machine learning datasets. An Azure Machine Learning Dataset is a resource that allows you to manage, version, and monitor your data within Azure Machine Learning.

In the program below, we will set up a new dataset in Azure Machine Learning using the `azure-native.machinelearningservices` package from Pulumi. I'll guide you through the necessary steps:

1. Set up a new Azure Machine Learning Workspace, if not already available. This workspace is where we organize and manage our ML assets.
2. Create a Storage Account that will underpin our Datastore.
3. Establish a Datastore inside our ML Workspace, connecting it to our Storage Account.
4. Register a Dataset within our ML Workspace pointing to a source location in the Datastore where our training data resides.

Let's begin by creating a Pulumi program to set up the infrastructure described above:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure Resource Group to hold our resources
resource_group = azure_native.resources.ResourceGroup("resource_group")

# Create an Azure Storage Account for our datasets
storage_account = azure_native.storage.StorageAccount("storageaccount",
                                                     resource_group_name=resource_group.name,
                                                     kind="StorageV2",
                                                     sku=azure_native.storage.SkuArgs(name="Standard_LRS"))

# Create an Azure ML Workspace
workspace = azure_native.machinelearningservices.Workspace("workspace",
                                                           resource_group_name=resource_group.name,
                                                           sku=azure_native.machinelearningservices.SkuArgs(name="Basic"),
                                                           location=resource_group.location)

# Create an Azure ML Datastore that links to our Azure Storage Account
datastore = azure_native.machinelearningservices.Datastore("datastore",
                                                           name="mydatastore",
                                                           workspace_name=workspace.name,
                                                           resource_group_name=resource_group.name,
                                                           datastore_properties=azure_native.machinelearningservices.DatastorePropertiesResourceArgs(
                                                               storage_account_id=storage_account.id))

# Register an Azure ML Dataset within our ML Workspace
dataset = azure_native.machinelearningservices.MachineLearningDataset("dataset",
                                                                      dataset_name="mytrainingdataset",
                                                                      workspace_name=workspace.name,
                                                                      resource_group_name=resource_group.name,
                                                                      parameters=azure_native.machinelearningservices.MachineLearningDatasetParametersArgs(
                                                                          source=azure_native.machinelearningservices.SourceInfoArgs(
                                                                              datastore_name=datastore.name,
                                                                              source_uri=["path/to/your/training/data"]
                                                                          )
                                                                      ))

# Export the Azure ML Datastore and Dataset name
pulumi.export("datastore_name", datastore.name)
pulumi.export("dataset_name", dataset.name)
```

In the provided code:
- We have declared an Azure resource group, which is a container that holds related resources for an Azure solution.
- We then create a storage account, which is needed to store our datasets.
- We set up an Azure Machine Learning workspace, which is required to manage and orchestrate machine learning workflows.
- We establish a Datastore that is associated with the storage account. The Datastore in Azure ML is a place to store and retrieve datasets.
- We register a Dataset, pointing it to a location within the Datastore. This location will hold the training data used in machine learning model training.

Make sure you already have an Azure subscription and Pulumi CLI installed and configured. You would run this Pulumi program using `pulumi up` in your terminal, and it would provision the described infrastructure on Azure, ready for you to use for storing and versioning your training datasets. Remember to place your actual training data in the specified location ("path/to/your/training/data") in your storage account.