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

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

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

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Article provides a technical guide on using a specific tool (Pulumi) to manage cloud infrastructure (Vertex AI endpoints).
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  1. dev.to — LLM tag TIER_1 English(EN) · Multigrid ·

    Provisioning a Vertex AI Endpoint With Pulumi

    <p>The obvious Pulumi program — create a <code>gcp.vertex.AiEndpoint</code>, point it at a model, call <code>pulumi up</code> — does not exist, because that resource does not accept a model. Knowing which of the two real paths you are on before you write anything saves an afterno…

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