How to Use GCP Vertex AI: A Step-by-Step Guide

Leanware Editorial Team
Dec 23, 2025
9 min read

Getting machine learning models from notebooks into production involves managing data pipelines, training infrastructure, and deployment endpoints. Google Cloud's Vertex AI consolidates these workflows into a unified platform.

Let's explore how to set up Vertex AI and get your first model running, whether you’re using AutoML or custom training.

What Is Vertex AI?

Vertex AI is Google Cloud's managed machine learning platform that handles the entire ML lifecycle. Released in 2021, it combines tools for data preparation, model training, deployment, and monitoring in a single interface.

The platform supports both AutoML for no-code model development and custom training with frameworks like TensorFlow, PyTorch, and XGBoost.

You can build models for tabular data classification, image recognition, natural language processing, and video analysis. Vertex AI connects directly to BigQuery for data access and Cloud Storage for artifact management.

Why Use Vertex AI on Google Cloud Platform?

Vertex AI removes the infrastructure complexity that slows ML projects. Instead of managing separate systems for training, serving, and monitoring, you work in a unified environment that scales compute automatically. When training completes, resources shut down, so you only pay for active usage with 30-second billing increments.

The platform integrates with GCP services through consistent IAM policies. You query BigQuery tables directly in training jobs without exporting data. Feature Store maintains consistent feature definitions across training and serving. Model Registry tracks versions with lineage information showing which data and code produced each model.

Prerequisites for Using Vertex AI

Create and Configure a Google Cloud Project

You need a Google Cloud project before using Vertex AI. Navigate to console.cloud.google.com and select the project dropdown at the top. Click "New Project" and enter a project name. The system generates a unique project ID, which you'll reference in API calls and gcloud commands.

Projects isolate resources and billing between environments. Create separate projects for development, staging, and production to prevent accidental changes to live systems. Note your project ID from the project dashboard.

Enable Required APIs

Vertex AI requires several APIs. Go to console.cloud.google.com/apis/library and search for these services:

Vertex AI API for ML operations
Cloud Storage API for dataset and model storage
Compute Engine API for training infrastructure
Notebooks API for managed Jupyter environments

Click "Enable" for each API. The process takes 1–2 minutes per API. Check the API dashboard under "APIs & Services" to verify activation status. Attempting to use services before enabling their APIs returns permission errors.

Set Up Billing and Permissions

Link a billing account under the billing section. Google requires payment information for all services, including free tier usage. New accounts receive $300 in credits valid for 90 days, sufficient for learning Vertex AI fundamentals.

Assign IAM roles for team access. Grant "Vertex AI User" role for dataset creation, model training, and deployment.

Add "Storage Admin" for full Cloud Storage access. For production, use granular roles like "Vertex AI Model User" that limit permissions to specific operations. Set up billing alerts to track spending as you experiment.

Preparing Your Data for Vertex AI

Understanding Data Requirements

Vertex AI accepts multiple data formats. Tabular data uses CSV files with headers defining column names. Images require JPG or PNG format with associated label files. Text classification needs TXT or JSONL format with category labels. Video data uses MP4 or MOV format.

For tabular classification, structure your CSV with features in columns and one target column containing class labels. Each row represents a training example. AutoML requires at least 1,000 examples per class for reliable model quality. Custom training works with smaller datasets but benefits from more data.

Upload a Dataset to Google Cloud Storage

Create a Cloud Storage bucket for your data. Run this command in Cloud Shell or your terminal:

gsutil mb -l us-central1 gs://your-bucket-name

Replace your-bucket-name with a globally unique identifier. Upload your dataset:

gsutil cp local_data.csv gs://your-bucket-name/datasets/

For large files exceeding 1GB, use the -m flag for parallel uploads. Keep your bucket in the same region as your Vertex AI training jobs. Cross-region data transfer adds latency and costs $0.01 per GB.

Import the Dataset into Vertex AI

Open Vertex AI in the console and navigate to Datasets. Click "Create" and select your data type (Tabular, Image, Text, or Video). For tabular data, provide the Cloud Storage path to your CSV file and configure the schema.

Vertex AI scans your data and generates statistics showing feature distributions, missing values, and data types. The import process takes 5-20 minutes depending on dataset size. Review the statistics before training to catch issues like incorrect data types or high cardinality categorical features.

Training Models in Vertex AI

Train an AutoML Tabular Model

AutoML handles algorithm selection and hyperparameter tuning automatically. You specify the prediction target, and Vertex AI tests multiple model architectures to find the best performer.

Create a Dataset in Vertex AI

After importing data, select your dataset and click "Train New Model." Choose AutoML as the training method. Configure these settings:

Objective: Classification or Regression based on your target variable.
Target column: The column containing values to predict.
Training budget: Maximum hours for training (affects cost and model quality).

Vertex AI automatically detects column data types and suggests transformations like one-hot encoding for categorical variables.

Generate Data Statistics (Optional)

The Analyze tab shows feature statistics generated during import. Review distributions to identify skewed features or outliers. Check for class imbalance in classification tasks, which might require adjusting class weights during training.

Feature correlation matrices highlight redundant features. Vertex AI handles feature engineering internally, but understanding your data improves interpretation of results.

Start Training Your Model

Set your training budget based on dataset complexity. Small datasets (under 100MB with fewer than 20 features) complete in 1–2 hours. Larger datasets with hundreds of features need 4–6 hours for optimal results.

AutoML tabular training costs $1.90 per node hour in most regions. A typical 3-hour training job costs $5.70. Click "Start Training" and Vertex AI begins testing multiple algorithms including boosted trees, neural networks, and ensemble methods. Training runs asynchronously, sending email notification when complete.

Evaluating and Deploying Models

Evaluate Model Performance

After training completes, navigate to Model Registry and select your model. The Evaluate tab shows performance metrics specific to your task type.

For classification, review:

Precision: Percentage of positive predictions that were correct.
Recall: Percentage of actual positives the model identified.
F1 Score: Harmonic mean of precision and recall.
AUC-ROC: Model's ability to distinguish between classes.

For regression, check:

MAE (Mean Absolute Error): Average prediction error.
RMSE (Root Mean Squared Error): Standard deviation of residuals.
R-squared: Proportion of variance explained by the model.

The confusion matrix for classification shows prediction patterns across classes. High off-diagonal values indicate common misclassifications. Feature importance scores reveal which variables most influence predictions.

Deploy a Model with Vertex AI

Deploy your model to an endpoint for real-time predictions. Click "Deploy to Endpoint" in the model details page. Configure these options:

Setting	Recommendation	Cost Impact
Machine type	n1-standard-2 for light traffic	$0.095/hour
Minimum nodes	1 for production, 0 for dev	Continuous charge
Maximum nodes	3-5 for autoscaling	Scales with traffic

Deployment takes 10–15 minutes while infrastructure provisions. For production workloads, set minimum nodes to 2 for high availability. Development endpoints can use zero minimum nodes, accepting 2-3 minute cold start delays when first requests arrive.

Testing Your Deployed Model

Send Prediction Requests

Test your endpoint with the Vertex AI Python SDK:

from google.cloud import aiplatform

aiplatform.init(project='your-project-id', location='us-central1')

endpoint = aiplatform.Endpoint('projects/123/locations/us-central1/endpoints/456')

instances = [
    {'feature1': 5.1, 'feature2': 3.5, 'feature3': 1.4, 'feature4': 0.2}
]

predictions = endpoint.predict(instances=instances)
print(predictions.predictions)

Replace the endpoint path with your actual endpoint ID from the console. The response includes predicted values and confidence scores.

For REST API testing, get your endpoint URL from the console and send authenticated requests:

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://us-central1-aiplatform.googleapis.com/v1/projects/YOUR_PROJECT/locations/us-central1/endpoints/YOUR_ENDPOINT:predict \
  -d '{"instances": [{"feature1": 5.1, "feature2": 3.5}]}'

Monitor prediction latency in the endpoint metrics dashboard. Tabular models typically respond in 50-200ms. Latency above 500ms indicates the need for larger machine types or model optimization.

Best Practices and Tips

Choosing Between AutoML and Custom Models

AutoML fits most business use cases requiring standard classification or regression. It optimizes model selection and hyperparameters automatically, requiring no ML expertise. Use AutoML when:

You need quick results without deep ML knowledge.
Your problem matches standard supervised learning patterns.
Model interpretability through feature importance is sufficient.
You have at least 1,000 examples per class for classification.

Custom training provides control over model architecture and training process. Choose custom training when:

You need specific model behaviors not available in AutoML.
Implementing research papers or novel architectures.
Using transfer learning with pretrained models.
Working with specialized data types or loss functions.

Custom training costs vary by instance type. CPU training on n1-standard-4 costs $0.19/hour. GPU instances like n1-standard-8 with NVIDIA T4 cost $0.70/hour. Training a small model takes 30-90 minutes on CPU instances.

Cost Optimization Strategies

Control Vertex AI spending with these practices:

Set training budgets to prevent runaway costs. AutoML accepts maximum node hour limits. Most models reach optimal performance within 4–6 hours, with diminishing returns afterward.

Use batch prediction for offline workloads instead of persistent endpoints. Batch processing costs $0.04 per 1,000 predictions compared to endpoint hosting at $68-190/month depending on machine type.

Configure autoscaling with zero minimum nodes for development environments. Production endpoints need minimum nodes for availability but can scale down during low-traffic periods.

Delete unused resources regularly. Endpoints charge continuously while active. Remove old models from Model Registry to reduce storage costs at $0.04 per GB monthly.

Enable budget alerts in Cloud Billing Console. Set thresholds at 50%, 80%, and 100% of your monthly budget. Alerts notify you before costs exceed expectations.

Summary of Key Steps

Vertex AI unifies ML workflows in a managed platform. You created a GCP project, enabled required APIs, and configured billing with IAM roles. After uploading data to Cloud Storage and importing it into Vertex AI, you trained an AutoML model by selecting a target variable and setting a training budget.

Model evaluation provided performance metrics and feature importance scores. You deployed the model to an endpoint with autoscaling configuration and tested predictions using the Python SDK. Cost optimization involves choosing appropriate compute resources, using batch prediction for offline workloads, and cleaning up unused infrastructure.

Next Steps With Vertex AI

Explore Vertex AI Pipelines to automate ML workflows. Pipelines orchestrate data preprocessing, training, evaluation, and deployment steps that run automatically when new data arrives. Set up Model Monitoring to detect prediction drift and data quality issues in production deployments.

Integrate Feature Store to maintain consistent feature definitions across training and serving. Feature Store reduces training-serving skew by providing the same feature transformations in both environments. Review Google's MLOps guides for production best practices.

Try custom training with your preferred framework. Vertex AI supports TensorFlow, PyTorch, scikit-learn, and XGBoost through pre-built containers. For specialized requirements, build custom containers with your exact dependencies.

You can connect with us for help setting up Vertex AI, optimizing workflows, and scaling your models in production.

Frequently Asked Questions

What is Vertex AI used for?

Vertex AI builds, trains, and deploys machine learning models on Google Cloud. The platform provides AutoML for no-code development and custom training for advanced use cases. Organizations use it for classification, regression, natural language processing, computer vision, and recommendation systems across industries.

Is Vertex AI the same as AutoML?

No. Vertex AI is the complete ML platform containing AutoML as one training option. The platform includes custom model training with TensorFlow, PyTorch, and other frameworks, plus MLOps tools like pipelines, model registry, feature store, and monitoring. AutoML represents the no-code subset within Vertex AI.

How do I deploy a model in Vertex AI?

Navigate to your trained model in Model Registry and click "Deploy to Endpoint." Configure the machine type (n1-standard-2 for basic workloads) and autoscaling settings (1-3 nodes typical). Deployment takes 10–15 minutes. Alternatively, use gcloud CLI or the Vertex AI Python SDK for programmatic deployment.

How much does Vertex AI cost?

Vertex AI uses pay-as-you-go pricing. AutoML tabular training costs $1.90 per node hour. Custom training starts at $0.19/hour for CPU instances. Deployed endpoints cost $0.095-0.38/hour based on machine type. Predictions cost $0.04 per 1,000 requests for batch processing. New accounts receive $300 in credits valid for 90 days.

Do I need coding skills to use Vertex AI?

Not for AutoML. The console interface handles model building through graphical workflows. Upload data, select a target variable, and start training without writing code. However, custom training, advanced deployment configurations, production pipelines, and API integration require Python programming skills and familiarity with ML frameworks.

How much does Vertex AI actually cost for a typical ML project?

A small tabular classification project with 10GB of data costs $4-8 for AutoML training (2-4 node hours at $1.90/hour). Deploying one endpoint on n1-standard-2 costs $68/month. Adding 50,000 monthly predictions costs $2. Total monthly cost runs $75-85 for a single production model. Projects with multiple models, GPU training, or high prediction volume cost $300-1,000/month.

How do I migrate from AWS SageMaker to Vertex AI?

Export trained models from SageMaker to S3, then transfer to Google Cloud Storage using gsutil or Transfer Service. Both platforms support TensorFlow, PyTorch, and scikit-learn, enabling direct model transfer. Retrain models if you used SageMaker-specific features like built-in algorithms. Update prediction code from SageMaker SDK to Vertex AI SDK, changing endpoint initialization and request formats.

What are Vertex AI's quotas and limits?

Default quotas vary by region. Most regions allow 100 concurrent training jobs, 50 models per endpoint, and 10 endpoints per project. Training jobs have a 7-day maximum duration. Prediction requests limit to 1,500 per minute per endpoint. Request quota increases through console.cloud.google.com/iam-admin/quotas if your workload exceeds these limits.

Can Vertex AI handle real-time inference at scale?

Yes. Endpoints autoscale horizontally based on traffic, handling thousands of requests per minute. Configure autoscaling with minimum and maximum node counts. Vertex AI monitors request rates and scales within 2–3 minutes. For high-volume applications serving millions of daily predictions, use machine types with GPUs like n1-standard-8 with T4 accelerators, or deploy multiple endpoints behind Cloud Load Balancing.

Can I use Vertex AI with on-premises data?

Yes. Connect on-premises infrastructure to Google Cloud using Cloud VPN for encrypted tunneling or Cloud Interconnect for dedicated connections. Transfer large datasets physically using Transfer Appliance, which ships storage devices to Google for upload. Once data reaches Cloud Storage, Vertex AI accesses it directly through standard APIs. For ongoing synchronization, implement automated pipelines using Cloud Data Fusion or custom Python scripts with the Cloud Storage SDK.