An enterprise is designing a deployment workflow to integrate an open-source machine learning model discovered in Vertex AI Model Garden into an existing cloud-native application. The architecture must satisfy several key operational requirements: Continuous Delivery: Automate deployment workflows with progressive canary rollouts and fast rollback capabilities to minimize release risk. Scalable Serving: Provide low-latency, real-time online inference capable of automatically adapting to dynamic traffic demands. Security: Enforce least-privilege identity access so the application service account can securely invoke model prediction APIs. Which deployment and integration strategy should you recommend?

Automate packaging and progressive canary deployments to a Vertex AI endpoint using Cloud Build and Cloud Deploy, configure accelerator-optimized compute instances with autoscaling, and grant the application service account the Vertex AI User (roles/aiplatform.user) role.: ### What this solution is This architecture leverages Google Cloud's native CI/CD and MLOps toolset to manage the deployment lifecycle of **Vertex AI Model Garden** assets. It combines **Cloud Build** for automated packaging and testing with **Cloud Deploy** for controlled delivery to **Vertex AI endpoints**, while securing model access using IAM roles. ### How it meets the requirements - **Continuous Delivery and Safe Releases**: **Cloud Deploy** provides structured release pipelines that support **canary release strategies** on **Vertex AI endpoints**. This allows you to route a subset of production traffic to a new model version, evaluate performance and error rates, and seamlessly roll back if anomalies are detected. - **Scalable Serving**: Deploying the model to a managed **Vertex AI endpoint** with tailored machine types and accelerators (such as NVIDIA GPUs) ensures high-throughput, low-latency inference. Vertex AI prediction nodes automatically autoscale based on incoming request metrics to handle demand fluctuations. - **Security**: Applying the **Vertex AI User** (`roles/aiplatform.user`) role to the client application's dedicated service account satisfies the principle of least privilege, enabling secure token-based authentication to invoke online predictions without exposing credentials. ### Key advantages and capabilities - **Automated orchestration**: Cloud Build automates testing, container validation, and evaluation against quality benchmarks before triggering release pipelines. - **Fine-grained traffic management**: Vertex AI endpoints natively allow multiple model versions to share a single endpoint while splitting traffic percentages during canary stages. - **Managed infrastructure**: Vertex AI abstracts node management, health checking, and underlying hardware provisioning for prediction workloads. - **Enterprise observability**: Seamlessly integrates with Cloud Monitoring and Cloud Logging for monitoring latency, throughput, and error rates. ### Why this is optimal This approach aligns with Google Cloud architecture best practices for operational excellence in machine learning. It avoids manual operational overhead and fragmented infrastructure while delivering an enterprise-grade serving environment tailored for foundation and open-source models.

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Deploy Model Garden Vertex Ai Endpoint

Section: Integrating AI models from Model Garden into the solution

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An enterprise is designing a deployment workflow to integrate an open-source machine learning model discovered in Vertex AI Model Garden into an existing cloud-native application. The architecture must satisfy several key operational requirements:

Continuous Delivery: Automate deployment workflows with progressive canary rollouts and fast rollback capabilities to minimize release risk.
Scalable Serving: Provide low-latency, real-time online inference capable of automatically adapting to dynamic traffic demands.
Security: Enforce least-privilege identity access so the application service account can securely invoke model prediction APIs.

Which deployment and integration strategy should you recommend?

Export the model artifacts to Cloud Storage, deploy them on Compute Engine using preemptible Spot VMs in an unmanaged instance group, and embed static API keys in the client application headers for authentication.

Deploy the Model Garden model container directly to Cloud Run behind an external Application Load Balancer with Cloud CDN caching enabled, and assign the BigQuery Admin role to the calling service account.

Automate packaging and progressive canary deployments to a Vertex AI endpoint using Cloud Build and Cloud Deploy, configure accelerator-optimized compute instances with autoscaling, and grant the application service account the Vertex AI User (roles/aiplatform.user) role.

Register the model in BigQuery ML using a SQL CREATE MODEL statement, execute scheduled recurring batch queries every minute for predictions, and assign the Vertex AI Feature Store Admin role to the service account.

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