Pulumi guides Vertex AI endpoint provisioning for custom and pre-trained models

This article details how to provision Vertex AI endpoints using Pulumi, highlighting two primary methods. The first involves using the `gcp.vertex.AiEndpointWithModelGardenDeployment` resource for models from Model Garden or Hugging Face, which handles both endpoint creation and model deployment. The second method, for custom-trained models, requires a multi-step process: declaratively creating the endpoint with `gcp.vertex.AiEndpoint`, uploading the model artifact, and then triggering the model deployment via a command resource. AI

IMPACT Provides developers with a clear path to deploy AI models on Google Cloud infrastructure using infrastructure-as-code.

RANK_REASON Article provides a technical guide on using a specific tool (Pulumi) to manage cloud infrastructure (Vertex AI endpoints).

Read on dev.to — LLM tag →

• gcp.vertex.AiEndpoint
• gcp.vertex.AiEndpointWithModelGardenDeployment
• Hugging Face
• Model Garden
• Nvidia L4
• PaliGemma
• Pulumi
• Vertex AI

• infra
• product

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