1. Storing and Sharing AI Dataset Annotations.


    To store and share AI dataset annotations, you'll need to manage datasets effectively within a cloud environment. For example, consider using Google Cloud's Vertex AI services, which provide resources like AiDataset and AiMetadataStore to handle datasets for machine learning.

    The gcp.vertex.AiDataset resource facilitates storing datasets, including labeling and categorizing data, which is crucial for training machine learning models. The datasets can include various types of data, such as images, videos, text, or tabular data, which you can then annotate accordingly.

    The gcp.vertex.AiMetadataStore resource allows you to create a centralized metadata repository where your machine learning assets, including datasets and their annotations, can be stored and shared with your team or collaborators. It maintains the relationships between these assets, making it easier to manage complex machine learning workflows.

    Let's create a Pulumi program in Python that sets up an AI dataset for annotations and a metadata store for collaboration in Google Cloud Platform (GCP).

    Here's what the program will do:

    1. Create an AI dataset on Google Cloud using gcp.vertex.AiDataset.
    2. Create a Vertex AI metadata store using gcp.vertex.AiMetadataStore.

    Remember, you need to have the GCP project and region configured with Pulumi and have appropriate permissions to create these resources.

    Here's the detailed Pulumi program:

    import pulumi import pulumi_gcp as gcp # Replace these variables with your own data information project_id = "my-gcp-project-id" region = "us-central1" dataset_display_name = "my-ai-dataset" metadata_store_name = "my-metadata-store" # Create the AI dataset for storing and annotating data ai_dataset = gcp.vertex.AiDataset("myAiDataset", project=project_id, region=region, display_name=dataset_display_name, metadata_schema_uri="gs://google-cloud-aiplatform/schema/dataset/metadata/[YOUR_SCHEMA_HERE].json" # Add other properties like labels, description, etc., as needed ) # Create the Vertex AI metadata store for sharing the dataset annotations ai_metadata_store = gcp.vertex.AiMetadataStore("myAiMetadataStore", name=metadata_store_name, region=region, project=project_id # Add other optional properties like description, etc., as needed ) # Export the IDs of the dataset and metadata store to access them later pulumi.export("ai_dataset_id", ai_dataset.id) pulumi.export("ai_metadata_store_id", ai_metadata_store.id)

    This program starts by importing the necessary Pulumi modules for Python. It then specifies the project ID, region, and names for the dataset and metadata store.

    We create an AI dataset resource, specifying the schema URI, which is a Google Cloud Storage URL pointing to the schema that describes the format of the dataset. Replace [YOUR_SCHEMA_HERE] with the schema relevant to your datasets, such as image, video, text, etc.

    Next, we create an AI metadata store. It's a simple resource in this example, but you can customize it with various properties like encryption specifications or descriptions as needed.

    At the end of the program, we're exporting the dataset and metadata store IDs as stack outputs so that you can reference them in other operations, like accessing your newly created dataset and metadata store in the Google Cloud console or in other Pulumi programs.

    By running this Pulumi program, the specified AI dataset and metadata store will be provisioned in your GCP project, ready for you to start uploading your data and annotations, and sharing them with your collaborators.