Before diving into features, let us understand what Vertex AI is and what problem it solves.

The one-sentence definition

Vertex AI is Google Cloud’s fully managed, unified platform for building, deploying and managing machine learning models and generative AI applications. It covers the whole journey: finding or training a model, testing it, putting it into use, watching how it performs, and keeping it secure and governed. You work through a web console, programming libraries and APIs, and Google runs the underlying machines.

What is machine learning, briefly?

Machine learning is a way of teaching computers to find patterns from examples instead of following hand-written rules. Show a system thousands of labeled photos of cats and dogs, and it learns to tell them apart. The result of this learning is called a model. Generative AI is a branch in which models create new content, such as text, images, code or summaries, in response to a request called a prompt.

The problem Vertex AI solves

Building a working AI feature involves far more than a clever model. You need data preparation, a place to experiment, machines to train on, a way to serve answers fast, checks that the model stays accurate, and strong security. Before unified platforms, teams assembled these pieces by hand, and many promising prototypes never reached production. Vertex AI aims to provide these pieces together, with consistent security and billing.

Understanding where Vertex AI came from explains why it is organized the way it is.

Before Vertex AI, Google Cloud offered several separate machine learning products: one for training, another for prediction, others for specific kinds of data such as images or text. Each had its own interface, its own rules and its own way of handling data. Teams struggled to move a model from experiment to production because every step crossed a boundary between tools.

Vertex AI brought these services together under one roof, with shared data handling, a common model registry and consistent security. As generative AI took off, the platform grew to include a catalog of foundation models, tools for prompt design and tuning, and frameworks for building AI agents. Today it serves both classic machine learning, such as predicting customer churn, and modern generative AI, such as answering questions over company documents.

Vertex AI contains many parts. A map first makes each later chapter easier to place.

flowchart TD
  D[Data in BigQuery and Cloud Storage] --> W[Notebooks and Experiments]
  D --> MG[Model Garden and Generative AI Tools]
  W --> T[Training: AutoML or Custom]
  MG --> TU[Prompting, Grounding and Tuning]
  T --> R[Model Registry]
  TU --> R
  R --> E[Endpoints and Batch Prediction]
  E --> A[Applications and Users]
  E --> M[Monitoring and Evaluation]
  M --> D
        

For many projects, the best first step is not to train anything but to use a model that already exists.

What Model Garden offers

Model Garden is a library inside Vertex AI where you can discover, test and use models. It includes Google’s own Gemini family of models, open models such as Gemma and other open-weight options, and models from partner companies that are offered through the platform. You can try a model in the browser, call it through an API, deploy an open model on your own endpoint, or tune it with your data. The exact catalog changes frequently, so check the console for what is currently available in your region.

Ways to consume a model

This is the area most people mean today when they say “AI”. Here are its main building blocks.

Prompts and the studio

A prompt is the instruction you give a model. Vertex AI Studio is a browser-based workspace where you write prompts, adjust settings such as how creative or strict the answers should be, test with different inputs, and compare models side by side. When you are happy with a prompt, you can copy the settings into your application code.

Multimodal models

Many modern models are multimodal, meaning they can understand and combine several kinds of input, such as text, images, audio, video and documents. You can, for instance, send a photo of a damaged product along with a question and receive a description and suggested next steps.

Important controls

Context windows and tokens

Models read and write in small chunks called tokens, which are roughly pieces of words. Each model can consider only a limited amount of text at once, called its context window. Billing for generative models is usually based on the number of tokens in the input and the output, so long prompts and long answers cost more.

A model only knows what it learned during training. Grounding connects it to your own, current and trusted information.

The problem: stale and generic knowledge

A model does not know your company’s policies, last week’s prices or private documents. If you ask it, it may guess. Grounding solves this by giving the model relevant, trusted information at the moment of the question, so answers are based on facts you provide.

Embeddings: turning meaning into numbers

An embedding is a list of numbers that captures the meaning of a piece of text or an image. Items with similar meaning get similar numbers. This lets software find related content by comparing numbers, even when the exact words differ. For example, “reset my password” and “I forgot my login” end up close together.

Vector search

Vertex AI Vector Search is a managed service for storing embeddings and finding the nearest matches very quickly, even among billions of items. It powers semantic search, recommendations and the retrieval step of question-answering systems.

Retrieval-augmented generation (RAG)

RAG is a pattern that combines search with generation. When a question arrives, the system first retrieves the most relevant passages from your documents, then gives them to the model along with the question, and the model writes an answer based on those passages. This reduces made-up answers, keeps knowledge current without retraining, and allows citations back to sources.

sequenceDiagram
  participant U as User
  participant A as Application
  participant V as Vector Search
  participant M as Generative Model
  U->>A: Asks a question
  A->>V: Find passages similar to the question
  V-->>A: Most relevant document passages
  A->>M: Question plus retrieved passages
  M-->>A: Grounded answer with sources
  A-->>U: Answer
        

When prompting and grounding are not enough, you can adapt a model to your needs. Choose the lightest method that works.

What tuning is good for

Beyond answering questions, modern applications let models take actions. Vertex AI offers tools for building such agents.

What is an AI agent?

An AI agent is a system that uses a model to decide what to do next, and can use tools to do it: searching documents, calling business systems, running calculations or asking another agent for help. It works toward a goal over several steps rather than producing a single reply.

Search and conversation applications

For common needs such as company search or customer-service chat, Vertex AI offers higher-level tools that connect to your websites, documents and databases and provide search and conversational answers with far less building. These are a good starting point when your need matches a standard pattern, and you can move to custom agents when you need more control.

Not every problem is solved by a foundation model. Predicting numbers, spotting fraud or forecasting demand often calls for models trained on your own structured data.

AutoML: low-code training

AutoML trains models for you. You provide a labeled dataset, choose the kind of task, and the service tries various approaches and picks a good one. It suits teams without deep machine learning expertise, or anyone who wants a strong baseline quickly. The kinds of data and tasks supported change over time, so check current documentation for what is offered.

Custom training: full control

With custom training you bring your own training code, written with frameworks such as TensorFlow, PyTorch, XGBoost or scikit-learn, and Vertex AI runs it on managed machines. You choose the machine types, including GPUs and other accelerators, and can spread training across many machines. You can use ready-made containers for popular frameworks or supply your own container for complete flexibility.

Useful training features

Good models start with good data and careful experimentation.

Where data lives

Most Vertex AI projects draw data from BigQuery for tables and Cloud Storage for files such as images, audio and documents. Vertex AI can create managed datasets that point to this data, and many training and prediction services read directly from these locations.

Notebooks for exploration

Notebooks are interactive documents that mix code, results and notes, loved by data scientists for exploring data and trying ideas. Vertex AI offers managed notebook environments, including Vertex AI Workbench and Colab Enterprise, that come with common libraries installed, connect to your data securely, and shut down when idle to save money.

Experiments and tracking

When you try many versions of a model, it is easy to lose track of which settings produced which result. Vertex AI Experiments records the parameters, data versions and metrics of each run, and lets you compare them side by side. Visualization tools help you watch training progress over time.

A model that works once on a laptop is a prototype. A repeatable, automated process is what turns it into a product.

What MLOps means

MLOps applies the habits of software engineering, such as version control, automated testing and continuous delivery, to machine learning. Its goals are to make model building reliable, repeatable and easy to update as data changes.

Pipelines

Vertex AI Pipelines runs workflows made of connected steps, such as fetching data, validating it, training, evaluating, and deploying only if quality is good enough. It is a serverless service, so you do not manage the machines that run the steps. Pipelines are defined with open frameworks and each run is recorded with its inputs, outputs and lineage.

flowchart LR
  A[Ingest and Validate Data] --> B[Prepare Features]
  B --> C[Train Model]
  C --> D[Evaluate Model]
  D --> E{Meets Quality Bar?}
  E -->|Yes| F[Register Model]
  F --> G[Deploy to Endpoint]
  E -->|No| H[Stop and Alert Team]
        

Model Registry

The Model Registry is a central catalog of your models and their versions. It stores details such as who trained a model, with which data and settings, and how it scored in evaluation. From the registry you can deploy a chosen version, roll back to an earlier one, or compare versions.

Continuous training and delivery

Models eat features. Managing them well prevents one of the most common causes of production failures.

What is a feature?

A feature is an input signal for a model, such as a customer’s number of orders in the last 30 days or the average temperature this week. Creating good features is often the biggest factor in a model’s quality.

The training-serving skew problem

Teams often compute features one way during training, in bulk queries, and another way in production, in application code. Small differences between the two cause a model to behave worse live than in testing. This mismatch is called training-serving skew.

Vertex AI Feature Store

Vertex AI Feature Store is a managed service for defining, storing and serving features. It keeps one definition of each feature and can provide it both in bulk for training and with low delay for live predictions. Newer versions build on BigQuery as the offline source, so existing warehouse tables can become features without copying data into a separate system. It also supports reuse across teams, so people do not rebuild the same feature again and again.

A trained model is only valuable when it makes predictions for real people and systems.

Online prediction with endpoints

An endpoint is a service address that applications call to get predictions with low delay, usually within milliseconds to seconds. You deploy one or more model versions to an endpoint, choose the machine types, and set minimum and maximum numbers of machines. Autoscaling adds capacity when traffic rises and removes it when traffic falls, within your limits.

Batch prediction

When you do not need instant answers, such as scoring a million customers overnight, batch prediction processes large files or tables in bulk and writes results to storage or BigQuery. It is usually cheaper than keeping an endpoint running, because machines run only for the duration of the job.

A model is not finished when it is deployed. The world changes, and models must be watched.

Evaluating models before release

For classic models, evaluation uses measures such as accuracy, precision, recall and error on held-out test data. For generative models, quality is harder to measure. Vertex AI provides evaluation services that compare outputs against reference answers or use model-based judging on criteria such as correctness, helpfulness and safety. Build a fixed set of realistic test questions and rerun it whenever you change a prompt, model or setting.

Monitoring for drift and skew

Model monitoring watches live prediction traffic for two main problems. Training-serving skew means live inputs differ from the training data. Drift means the data changes gradually over time, for example when customer behavior shifts. Alerts tell you when thresholds are crossed so you can investigate or retrain.

Explainable AI

Explainability tools show which inputs influenced a prediction most. For example, they might show that a loan decision was driven mainly by income and payment history. This builds trust, helps debugging and supports regulatory requirements in some industries.

Powerful models require careful handling. Safety is a design requirement, not an afterthought.

AI systems handle valuable data and powerful capabilities, so they deserve the same protections as any critical system.

Identity and access

Access is managed through Identity and Access Management. Predefined roles cover common needs such as using models, running training jobs or administering endpoints. Training jobs, pipelines and endpoints run as service accounts, so give each a dedicated account with only the permissions needed, rather than broad default accounts.

Protecting sensitive data in prompts

Be deliberate about what goes into a prompt. Remove or mask personal and confidential information that the task does not need, use tools that detect and redact sensitive data, and avoid placing secrets such as passwords in prompts or tuning data.

AI workloads can become expensive quickly. Knowing the meters keeps surprises small.

Ways to control cost

With so many options, a clear way to choose saves time and money.

flowchart TD
  Q1{Is the task generating or understanding language, images or documents?} -->|Yes| Q2{Do you need your own private knowledge?}
  Q1 -->|No| Q5{Is it prediction on structured data?}
  Q2 -->|Yes| A1[Prompt plus grounding or RAG on a managed model]
  Q2 -->|No| Q3{Is prompting alone good enough?}
  Q3 -->|Yes| A2[Managed model API with good prompts]
  Q3 -->|No| A3[Consider tuning a model]
  Q5 -->|Yes| Q6{Need a quick baseline with little code?}
  Q6 -->|Yes| A4[AutoML or BigQuery ML]
  Q6 -->|No| A5[Custom training]
  Q5 -->|No| A6[Use a pretrained task-specific model]
        

Several Google Cloud services touch AI. Knowing the boundaries prevents confusion.

Patterns capture what experienced teams repeatedly find works; anti-patterns capture what goes wrong.

Helpful patterns

Anti-patterns to avoid

A checklist to carry into every project, followed by answers to common beginner questions.

Gemini is a family of models. Vertex AI is the platform on Google Cloud where you access those models, along with tools to tune, ground, deploy, evaluate and govern them, plus other models and classic machine learning services.

Not to begin. You can use managed models through prompts, or AutoML with little code. Deeper customization and custom training benefit from machine learning experience.

Use RAG when the gap is missing or changing knowledge. Use tuning when you need a consistent style, format or specialized task behavior. Many systems use both.

Google publishes data governance commitments for Vertex AI describing how customer data is handled. Check the current terms for your plan and region rather than assuming, especially for sensitive data.

It is a catalog inside Vertex AI where you can find, test and use Google models, open models and partner models, either through managed APIs or by deploying them yourself.

Online prediction returns answers within moments for live requests through a deployed endpoint. Batch prediction processes large datasets in bulk without needing a live endpoint, and is generally cheaper for non-urgent work.

Ground the model in trusted sources, write clear prompts, ask for citations, lower randomness settings, evaluate regularly and add human review for important outputs. No method removes the risk completely.

An embedding is a list of numbers that represents the meaning of text, an image or other content, so similar items are close together and can be found by comparing numbers.

They make model building repeatable and traceable. Pipelines automate and gate each step, and the registry tracks versions, so you can reproduce, compare, deploy and roll back models safely.

Choose smaller models where possible, shorten prompts, use caching and batch jobs, autoscale and undeploy idle resources, set quotas and budget alerts, and consider reserved capacity for steady high-volume traffic.

Yes. You can use private endpoints and Private Service Connect, along with VPC Service Controls and carefully scoped service accounts, to keep traffic and data within controlled boundaries.

Look at data first: has the input distribution changed, did an upstream source break, or are features computed differently from training? Then check recent changes to prompts, models and settings, and review monitoring alerts.

Vertex AI is Google Cloud’s unified platform for the full life of machine learning and generative AI. You can start with ready-made models from Model Garden, shape them with prompts, grounding and tuning, build search and agent applications, or train your own models with AutoML and custom training. Pipelines, the model registry, feature management, endpoints and batch prediction turn experiments into dependable services, while evaluation, monitoring and explainability keep them trustworthy over time. Security, responsible AI practices and cost controls are part of the design, not extras. The most reliable path is to begin with the simplest approach that meets a clear, measurable goal, add complexity only when evidence demands it, and keep watching the system after it goes live.