What is the use of Vertex AI in Google?

What is Vertex AI in Google?

Vertex AI is Google Cloud's unified machine learning platform that brings together all Google's AI/ML tools and services under one roof — enabling you to build, deploy, scale, and monitor AI models and applications in production.

What Vertex AI Provides

Using Vertex AI for Gen AI

pythonCopy
import vertexai
from vertexai.generative_models import GenerativeModel

# Initialize with your Google Cloud project
vertexai.init(project="your-project-id", location="us-central1")

# Use Gemini via Vertex AI
model = GenerativeModel("gemini-1.5-pro")

response = model.generate_content(
    "Explain RAG in simple terms with a Python example"
)
print(response.text)

RAG with Vertex AI Vector Search

pythonCopy
from google.cloud import aiplatform
from vertexai.language_models import TextEmbeddingModel

# Vertex AI provides managed vector search
embedding_model = TextEmbeddingModel.from_pretrained("text-embedding-004")

# Embed documents
def embed_text(texts: list[str]) -> list[list[float]]:
    embeddings = embedding_model.get_embeddings(texts)
    return [e.values for e in embeddings]

# Deploy to Vertex AI Vector Search (Matching Engine)
# This handles indexing, search, and scaling automatically

Vertex AI vs Direct API

Key Vertex AI Services for Gen AI Engineers

1. Model Garden — Access pre-trained models:

bashCopy
# Access Claude, Llama, Mistral, etc. via Vertex AI
gcloud ai models list --region=us-central1

2. Grounding — Connect to real-time data:

pythonCopy
from vertexai.preview.generative_models import (
    GenerativeModel, Tool, grounding
)

# Ground Gemini responses with Google Search or your data
model = GenerativeModel("gemini-1.5-pro",
    tools=[Tool.from_google_search_retrieval(
        google_search_retrieval=grounding.GoogleSearchRetrieval()
    )]
)

3. Agent Builder — Deploy production agents:

• Visual agent builder UI
• Connect to data stores (BigQuery, GCS, websites)
• Deploy with auto-scaling

When to Use Vertex AI

• Enterprise — need IAM, VPC, compliance, SLAs
• Google Cloud — already in GCP ecosystem
• Large scale — need auto-scaling inference
• MLOps — full ML pipeline (train → deploy → monitor)
• Multi-model — want access to many foundation models
• Tight Google integration — BigQuery, GCS, Cloud Run