1. What is Vertex AI's role in managing machine learning on GCP in TypeScript


    Vertex AI is a managed machine learning (ML) platform provided by Google Cloud that allows you to build, deploy, and scale ML models more efficiently. It offers various tools and services that support the entire ML workflow, including data preparation, model training, model evaluation, batch and online prediction, and model serving. Using Vertex AI, you can implement machine learning models within a unified API, which simplifies the process of integrating ML into your applications.

    In the context of Pulumi and infrastructure as code (IaC), you can define and manage your Vertex AI resources programmatically. This allows you to automate the provisioning of infrastructure needed for machine learning workloads and apply version control and CI/CD practices to your ML infrastructure.

    Here’s how you could use Pulumi with TypeScript to manage Vertex AI services in GCP:

    1. Feature Store: To organize and manage ML features for training and serving models.
    2. Tensorboard: To visualize and monitor ML experiments and training metrics.
    3. Metadata Store: To enrich and manage metadata for ML artifacts.
    4. Endpoint: To deploy and serve ML models for prediction.
    5. Dataset: To manage datasets that your models are trained on.

    Below is a basic TypeScript program using Pulumi. It demonstrates how to define Vertex AI resources such as an AI Endpoint and a Dataset on Google Cloud Platform. The program uses Pulumi’s GCP provider to create an AI Endpoint for deploying machine learning models and an AI Dataset to manage datasets used for model training.

    import * as gcp from "@pulumi/gcp"; // Define a Vertex AI Dataset resource const aiDataset = new gcp.vertex.AiDataset("my-ai-dataset", { displayName: "My Dataset", project: "my-gcp-project", region: "us-central1", labels: { env: "production", }, metadataSchemaUri: "gs://my-dataset-schema-uri", // additional configuration if necessary }); // Define a Vertex AI Endpoint resource const aiEndpoint = new gcp.vertex.AiEndpoint("my-ai-endpoint", { displayName: "My Endpoint", project: "my-gcp-project", region: "us-central1", labels: { env: "production", }, encryptionSpec: { kmsKeyName: "my-kms-key-name", }, // additional configuration if needed }); // Export the endpoint's name and dataset id for easy access export const endpointName = aiEndpoint.name; export const datasetId = aiDataset.id;

    In this program:

    • You define a new AI Dataset (AiDataset) which is a Vertex AI resource for managing datasets. The displayName, project, region, and metadataSchemaUri are properties required for creating the dataset. You've tagged the dataset with a label to denote the environment as 'production'.
    • Similarly, you define an AI Endpoint (AiEndpoint) which is a Vertex AI resource for deploying and serving ML models. The displayName, project, and region properties are set according to your GCP project and intended GCP region.
    • encryptionSpec is used to specify the KMS key for encryption, which enhances the security of your endpoint. Replace "my-kms-key-name" with the actual name of your KMS key.

    To run this program, you need to have Pulumi installed with your GCP configuration set accordingly, and replace placeholders like "my-gcp-project", "my-dataset-schema-uri", and "my-kms-key-name" with actual values from your GCP environment.

    This is a starter template. Depending on the complexity of your ML workflows and required resources, you would add more configurations, such as specifying models to deploy to the endpoint, more detailed dataset configurations, or perhaps a Model Monitoring Job to automatically watch for issues with your deployed models.

    By using Pulumi to define and deploy these resources, you can apply versioning, peer reviews, and track changes in your ML infrastructure just as you would with your application code. This practice enhances collaboration among team members and maintains consistency across different environments.