1. Hosting Vertex AI for ML model development and deployment


    Hosting Vertex AI requires setting up several resources that work together to build, train, and deploy machine learning models. Vertex AI is Google Cloud's integrated suite for AI that provides a seamless way for ML developers and data scientists to manage the complete lifecycle of AI development.

    If you want to use Pulumi to host Vertex AI for ML model development and deployment, you will need to use the Google Cloud (GCP) provider for Pulumi. The Pulumi Google Cloud (GCP) provider allows you to interact with many of the services that Google Cloud offers, including Vertex AI. Here's a program that sets up the essential resources needed for hosting Vertex AI:

    1. AI Platform Endpoint: Vertex AI Endpoints to deploy and serve models.
    2. AI Platform Model: A representation of a machine learning model in Vertex AI.
    3. AI Feature Store: For storing, serving, and managing features for your machine learning applications.

    Let's create a simplified Pulumi program to set up these elements for deploying a machine learning model.

    Before you get started, make sure you have authenticated with the GCP provider and set up your credentials. Pulumi automatically uses the GCP credentials set up on your machine, generally through gcloud auth login when using the gcloud CLI tool.

    Here's the program:

    import pulumi import pulumi_gcp as gcp # Project and region should be set according to your GCP project and preferred location for resources. project = 'your-gcp-project-id' region = 'us-central1' # Choose an appropriate region for your application. # Create an AI Platform Endpoint to deploy and serve machine learning models. ai_endpoint = gcp.vertex.AiEndpoint( "ai-endpoint", project=project, location=region, display_name="my_model_endpoint", ) # In this example, we're assuming that the model artifact is already in GCP's storage. # Usually, you will have a trained model artifact (for example, a TensorFlow SavedModel) stored in a GCS bucket. model_artifact_location = "gs://your-model-artifact-bucket/path-to-model-artifact" # Create an AI Platform Model. ai_model = gcp.vertex.AiModel( "ai-model", project=project, region=region, display_name="my_model", container_spec=gcp.vertex.AiModelContainerSpecArgs( image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-3:latest", model_uri=model_artifact_location, ), ) # Create an AI Feature Store, this is a registry for all your features for ML models. ai_feature_store = gcp.vertex.AiFeatureStore( "ai-feature-store", project=project, region=region, online_serving_config=gcp.vertex.AiFeatureStoreOnlineServingConfigArgs( fixed_node_count=1 ), # In a real-world setup, you'll want to configure encryption, store config, and TTL according to your security and application needs. ) # Output the endpoint for the deployed model. pulumi.export("endpoint", ai_endpoint.display_name) # Output the name of the Model Artifact GCS bucket location. pulumi.export("model_artifact_loc", model_artifact_location) # Output the name of the AI Feature Store. pulumi.export("feature_store", ai_feature_store.display_name)

    This Pulumi program sets up an AI Endpoint and a Model which are essential for deploying and hosting a machine-learning model on Vertex AI. The Feature Store is a bonus resource that can be used to store and serve model features for prediction, but is not strictly necessary if you're just deploying a pre-trained model.

    Remember, additional steps like model training, data management, and interaction with the deployed model for predictions would require more detailed setup and code. This program gives you the initial infrastructure that would be a part of those steps.