1. Scalable ML Pipelines with Vertex AI Pipelines


    Creating scalable machine learning (ML) pipelines with Vertex AI Pipelines involves automating and orchestrating the ML workflow—this includes data preprocessing, model training, evaluation, and deployment. Google's Vertex AI provides a managed service that helps you to construct, deploy, and scale ML pipelines quickly.

    Key Components of a Vertex AI Pipeline:

    1. AI Pipelines: Defines the workflow as a Directed Acyclic Graph (DAG) consisting of various steps like data ingestion, preprocessing, training, etc.
    2. AI Feature Store: Manages and serves machine learning features.
    3. AI Index: Utilizes similarity-based search on high-dimensional feature vectors.
    4. AI Endpoint: Hosts deployed ML models to serve predictions.
    5. AI Metadata Store: Tracks the metadata of artifacts and executions for pipelining.

    Here's an example Pulumi program that sets up the fundamental building blocks for such pipelines:

    import pulumi import pulumi_gcp as gcp # Set up a Vertex AI Feature Store. ai_feature_store = gcp.vertex.AiFeatureStore("aiFeatureStore", region="us-central1", project="my-gcp-project", # Details at: https://www.pulumi.com/registry/packages/gcp/api-docs/vertex/aifeaturestore/ ) # Create an AI Feature Store EntityType, i.e., a group of features related to a primary or composite key. ai_feature_store_entity_type = gcp.vertex.AiFeatureStoreEntityType("aiFeatureStoreEntityType", featurestore=ai_feature_store.name, project=ai_feature_store.project, # EntityType configuration options. # Documentation: https://www.pulumi.com/registry/packages/gcp/api-docs/vertex/aifeaturestoreentitytype/ ) # Define an AI Endpoint. This will host the machine learning model for online prediction. ai_endpoint = gcp.vertex.AiEndpoint("aiEndpoint", display_name="my-ml-model-endpoint", region="us-central1", project="my-gcp-project", # Endpoint configuration options. # Learn more at: https://www.pulumi.com/registry/packages/gcp/api-docs/vertex/aiendpoint/ ) # The following resources are likely to be embedded within the Vertex AI Pipeline logic # and hence are not set up independently in the same way as the above resources. # These are available in the gcp.vertex module and provide rich features for building ML applications: # - AiIndex: https://www.pulumi.com/registry/packages/gcp/api-docs/vertex/aiindex/ # - AiMetadataStore: https://www.pulumi.com/registry/packages/gcp/api-docs/vertex/aimetadatastore/ # - AiTensorboard: https://www.pulumi.com/registry/packages/gcp/api-docs/vertex/aitensorboard/ # Exposing key resources' details for external usage. pulumi.export("feature_store_name", ai_feature_store.name) pulumi.export("feature_store_entity_type_name", ai_feature_store_entity_type.name) pulumi.export("ai_endpoint_name", ai_endpoint.name) # Please note that actual pipeline orchestration involves creating a pipeline job, # typically using the Vertex AI SDK or CLI. This part is not handled directly within a Pulumi program.

    This program sets up the main components for an ML pipeline, although it doesn't create an ML workflow, which is often done through the Vertex AI SDK or CLI.

    You typically define your ML workflows using the Vertex AI SDK, which you then can orchestrate using Vertex AI Pipelines. Pulumi assists in setting up the resources needed for these workflows, such as the AI Feature Store and Endpoints, but the detailed workflow management is done externally.

    If you're looking to dive into the details of setting up ML Pipelines with Vertex AI, it's advisable to look into Vertex AI's documentation and quickstarts as well. Pulumi’s role is to provision and manage the cloud resources that your ML Pipelines will use.