What is Vertex AI Studio?

Google Cloud’s unified platform for:

• 🛠️ Building machine learning models
• 🔍 Exploring datasets
• ⚡ Training and deploying models
• 📊 Monitoring model performance

Vertex AI SDK is available for Java, Python, Go, and Node.js.

Setup Steps

Enable required APIs:

gcloud services enable \
aiplatform.googleapis.com \
compute.googleapis.com \
storage.googleapis.com

Install Python SDK

pip install google-cloud-aiplatform

Set authentication:

gcloud auth application-default login

Basic Model Deployment

from google.cloud import aiplatform

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

# Create an endpoint
endpoint = aiplatform.Endpoint.create(
 display_name="my-first-endpoint"
)

# Deploy a pre-trained model
model = aiplatform.Model.upload(
 display_name="text-classification-model",
 artifact_uri="gs://your-bucket/path/to/model"
)

deployed_model = model.deploy(
 machine_type="n1-standard-4",
 endpoint=endpoint
)
 

Making Predictions

Python SDK

response = endpoint.predict(
 instances=[
 {"content": "This product is amazing!"},
 {"content": "Terrible customer service"}
 ]
)
print(response.predictions)
 

REST API

POST https://us-central1-aiplatform.googleapis.com/v1/projects/your-project-id/
locations/us-central1/endpoints/your-endpoint-id:predict

Headers:
Authorization: Bearer $(gcloud auth print-access-token)
Content-Type: application/json

Body:
{
 "instances": [
 {"content": "Sample text for classification"}
 ]
}
 

Concepts

Term	Description
Endpoint	Deployed model serving predictions
Model Registry	Central repository for ML models
Pipeline	Automated ML workflows

Best Practices

• 📦 Use Vertex ML Metadata for tracking experiments
• 💾 Store datasets in Cloud Storage buckets
• 📉 Monitor predictions with Vertex Monitoring
• 🔐 Implement IAM roles for security

Troubleshooting

Error	Solution
Permission Denied	Check IAM roles for Vertex AI User
Quota Exceeded	Request quota increase in GCP console
Model Deployment Failed	Verify container requirements