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.
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.
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.
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