1. AutoML Model Training on Vertex AI


    AutoML on Google Cloud's Vertex AI allows you to train high-quality custom machine learning models with minimal effort and machine learning expertise. The AutoML workflow consists of several steps which include creating datasets, training the model, and deploying the model to an endpoint for prediction. Here, we'll use Pulumi to provision the necessary infrastructure to accomplish these steps on Vertex AI.

    Before diving into the code, here's a step-by-step explanation of what we'll achieve with the Pulumi program:

    1. Create an AI Dataset: We use the AiDataset resource to create a dataset within Vertex AI to store our data for training.
    2. Create an AutoML Training Job: After the dataset is ready, we'll create a training job. Although the specifics of an AutoML training job are not directly exposed as Pulumi resources, you can create a custom training job and then use Google Cloud's client libraries or REST API within Pulumi's dynamic providers to manage the AutoML training job.
    3. Deploy the Model to an Endpoint: Once the model is trained, we use AiEndpoint to create an endpoint. We then deploy the trained model to this endpoint for serving predictions.

    Now, let's translate these steps into a Pulumi program in Python:

    import pulumi import pulumi_gcp as gcp # Replace these variables with your own configuration details project_id = 'your-gcp-project-id' dataset_display_name = 'your-dataset-display-name' model_display_name = 'your-model-display-name' # Create Vertex AI Dataset ai_dataset = gcp.vertex.AiDataset("ai-dataset", project=project_id, display_name=dataset_display_name, metadata_schema_uri="gs://google-cloud-aiplatform/schema/dataset/metadata/image_1.0.0.yaml" ) # Normally, the next step would be to create an AutoML training job. At the time of writing, there is no dedicated # Pulumi resource for creating an AutoML training job directly. However, you can use the Pulumi dynamic provider # to interact with the Google Cloud Platform (GCP) to create the training job using the GCP SDK. # You would define a dynamic resource that interacts with GCP's AutoML APIs to create and manage the training job. # Here, we only have a placeholder comment to indicate where this action would occur. # For a full implementation, please refer to the Google Cloud AutoML documentation and Pulumi's dynamic providers. # Placeholder for AutoML training job creation and management # You would use GCP SDK to train the AutoML model using the created dataset here # Finally, create an AI Endpoint to deploy the model for serving predictions # Again, as an example, we are creating an endpoint to show the workflow. # The actual model deployment to this endpoint would take place after the model is trained with AutoML. ai_endpoint = gcp.vertex.AiEndpoint("ai-endpoint", project=project_id, display_name=model_display_name ) # Export the AI Endpoint URL, which will be used to make predictions once the model is deployed pulumi.export("endpoint_url", ai_endpoint.deployed_models.apply(lambda models: models[0].id if models else "No model deployed"))

    In this program, we've demonstrated how to initiate the process of creating AutoML models with Pulumi. Due to the limitations of direct integration for AutoML training in Pulumi, we've utilized placeholders to indicate the steps needed to train a model using GCP's SDK or APIs.

    After training your model, you would deploy the model to the created AiEndpoint. The deployment step typically involves interacting with Google Cloud's client libraries for Vertex AI or the REST API to deploy the trained model, which can proceed outside of Pulumi or with a dynamic provider within Pulumi.

    For a full integration, including automation of the training and deployment processes, you would combine Pulumi's infrastructure-as-code approach with the Google Cloud SDK in Python, allowing you to script the training and deployment steps. This can be a complex task suited for users with familiarity with both Pulumi dynamic providers and the Google Cloud Vertex AI service.

    For reference on how to interact with GCP's AutoML APIs using client libraries, please refer to the Google Cloud Vertex AI documentation. To learn more about dynamic providers in Pulumi, visit Pulumi's Dynamic Providers documentation.