---
title: "Vertex AI | Skills We Assign For | Azendo"
description: "Vertex AI as a managed ML platform — BigQuery integration, foundation models, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/vertex-ai/"
---

[Skills](https://azendo.co/skills/) MLOps and model delivery 

# Vertex AI.

Vertex AI is Google Cloud's unified machine learning platform, covering training, tuning, model registry, endpoints, pipelines, feature store and access to Google's foundation models.

## Where Vertex AI fits on a long engagement.

BigQuery integration is the practical differentiator for organisations already using it. Training directly on warehouse data without an export step removes a pipeline and the staleness that comes with it, which is a meaningful simplification.

Access to Gemini and other foundation models within the same platform, under the same IAM and billing, matters for organisations where a separate vendor relationship is a procurement or compliance obstacle rather than a preference.

## What an assigned team does with Vertex AI.

Pipelines are the component worth adopting early. Training expressed as a versioned, reproducible pipeline rather than a notebook someone runs is the transition that makes machine learning dependable.

Making that transition deliberately rather than when a handover forces it is part of the standards agreed as a [committed monthly capacity](https://azendo.co/pricing/).

## What we use Vertex AI for.

* Training directly on warehouse data BigQuery as the source, removing an export step and its staleness.
* Foundation models inside the estate Same IAM and billing, where a separate vendor is a compliance obstacle.
* Pipelines replacing notebooks Reproducible training that does not depend on a person running it.

## How Vertex AI capacity is assigned.

Vertex work is assigned inside AI capacity, with the move from notebooks to pipelines treated as a delivery milestone.

## Roles we assign Vertex AI for

* [MLOps Engineer MLOps engineering](https://azendo.co/services/mlops-engineering/mlops-engineer/)

## Service lines it sits in

* [MLOps engineering](https://azendo.co/services/mlops-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in mlops and model delivery

* [Weights & Biases — skill we assign for](https://azendo.co/skills/weights-and-biases/)
* [Comet — skill we assign for](https://azendo.co/skills/comet/)
* [Neptune.ai — skill we assign for](https://azendo.co/skills/neptune-ai/)
* [DVC — skill we assign for](https://azendo.co/skills/dvc/)
* [Feast — skill we assign for](https://azendo.co/skills/feast/)
* [feature stores — skill we assign for](https://azendo.co/skills/feature-stores/)
* [Feature engineering — skill we assign for](https://azendo.co/skills/feature-engineering/)
* [cross-validation — skill we assign for](https://azendo.co/skills/cross-validation/)

## Tell us what your roadmap needs Vertex AI for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)