What is Google Vertex AI? A plain-English explainer
Google Vertex AI is Google Cloud’s unified managed platform for building, training and deploying machine-learning models, including access to generative AI and foundation models. Rather than a single tool, it brings together the separate stages of the machine-learning lifecycle — preparing data, training a model, evaluating it and putting it into production — under one platform, a discipline commonly called MLOps that we cover in our what is MLOps guide. Vertex AI also covers the two very different jobs of training a model from scratch versus simply calling one that already exists, a distinction we explain in our training vs inference guide. This article covers what Vertex AI is, what it is used for, who uses it, and how it fits alongside similar platforms on other clouds.
A platform, not a single tool
Before a service like Vertex AI existed, building a machine-learning system meant stitching together separate tools for data preparation, model training, experiment tracking, deployment and monitoring — often across different teams and infrastructure. Vertex AI unifies these stages into one managed platform, so a data science team can move from a dataset to a deployed, callable model without leaving one environment or hand-building the plumbing between each step.
What Vertex AI is used for
Vertex AI serves two related but distinct purposes. The first is building and training custom models — supplying your own data and training a model tailored to a specific problem, then deploying it so applications can call it. The second is accessing foundation models: large, general-purpose models already trained by Google that you can use directly for generative AI tasks — generating text or answering questions, for example — without training anything yourself. We explain what a foundation model actually is, and how it differs from a custom-trained one, in our foundation model explainer.
Both purposes sit under the same platform because, in practice, teams often do both: using a foundation model as a starting point, then adapting or building around it with their own data and custom models.
Who uses Vertex AI
Vertex AI is aimed at data scientists and machine-learning engineers — people building and productionising models — rather than end users of an application. Application developers typically consume the models Vertex AI produces (or the foundation models it exposes) through an API, without needing to understand the training process underneath. The platform is built to support the specialist work of the ML team while giving the rest of the organisation a straightforward way to call the result.
How it compares across clouds
The pattern across the major clouds is similar: a platform for building and training your own models, alongside a separate route to simply using ready-made foundation models. On AWS, Amazon SageMaker is the platform for building and training custom models, while Amazon Bedrock is the route to using foundation models directly; on Azure, Azure Machine Learning plays the building-and-training role, with its own foundation-model access built in. Vertex AI is Google Cloud’s version of both halves of that pattern in one platform, rather than two separately named services.
The underlying idea — separate the job of building a model from the job of simply using one — is consistent across vendors, even though the product names and boundaries differ.
When you would use Vertex AI
Reach for Vertex AI’s custom-training side when you have your own data and a specific prediction problem a general-purpose model cannot solve out of the box. Reach for its foundation-model side when the task is more general — generating or understanding text, for instance — and an existing large model is likely to perform well without any training of your own. Many real projects start with a foundation model and only invest in custom training once a clear, data-backed reason to do so emerges.
Where Vertex AI shows up in certification study
Vertex AI appears in Google Cloud’s certification tracks wherever AI and machine learning are covered, including foundational coverage in the Associate Cloud Engineer syllabus and deeper treatment in AI-focused certifications such as Google’s own Generative AI Leader track. The broader distinction between building models and using foundation models also underpins AWS’s AI Practitioner certification. Our GCP study material covers Vertex AI at the depth each exam expects.
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