Vertex AI specialists for your dedicated team.

Where Vertex AI fits in a long-term product team.

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 where a separate vendor relationship is a procurement or compliance obstacle.

What your dedicated team does with Vertex AI.

Pipelines are the component worth adopting early. Training expressed as a versioned, reproducible pipeline is the step that makes machine learning dependable.

Making that step deliberately, well before a handover forces it, is part of the standards agreed as a committed monthly capacity.

Common Vertex AI use cases.

• 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.

Adding Vertex AI skills to your team.

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

At Azendo, Vertex AI work sits with a dedicated MLOps team. You can add Vertex AI capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.