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Vertex AI

The end-to-end ML platform and the Gemini models: from notebook to production model, with enterprise governance.

FOCUS · GOOGLE'S AI, GOVERNEDGemini, the Model Garden and the agents: the platform for getting serious
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01 · What it is

Vertex AI & Gemini, made clear.

Vertex AI is the AI workbench on Google Cloud: the Gemini models (and the Model Garden with Claude, Llama, Mistral), RAG with grounding on your data or on Google Search, the Agent Builder for agents, and complete MLOps — pipelines, registry, monitoring — for home-made models.

Gemini
Google's flagship models: multimodal, huge context windows
Garden
an open catalog: Claude and the open models too, same platform
Grounding
answers anchored to YOUR data or to Google Search: fewer hallucinations
Vertex AI & Gemini
OFFICIAL GOOGLE CLOUD BRANDING · VERTEX AI
INTERFACCIA REALE · VERTEX AI STUDIO · FONTE: GOOGLE CLOUD BLOG
REAL INTERFACE · VERTEX AI STUDIO · SOURCE: GOOGLE CLOUD BLOG
02 · How to use it well

The things that make the difference.

La piattaforma

Copilots, agents, automationsthe business use cases
Gemini & Garden
RAG & Search
Agent Builder
models · context · action
MLOps: pipelines, registry, evalsthe governed lifecycle
TPUs/GPUs underneath, IAM aroundpower and perimeter
From the experiment to the production service

The use case that pays back

Document search, support, data extraction: you start where ROI is measured in weeks.

RAG with foundations

Vertex AI Search on your documents: Google-quality retrieval, permissions included.

Continuous evaluations

Groundedness and quality measured at every release: AI commissioned like software.

Costs per request

The right model for the right task (Flash vs Pro): the AI bill is designed.

03 · In depth

Model Garden, tuning and MLOps

Vertex AI unifies the ML/GenAI cycle: the Model Garden exposes Gemini, the open models (Llama, Mistral) and yours; tuning (LoRA/supervised) specializes on your domain; Pipelines orchestrate training and deploy; the Feature Store serves features online; Model Monitoring intercepts drift and skew. For GenAI: grounding on Google Search or your data, a managed RAG Engine, provisioned throughput for production and the same no-training-on-your-data guarantees.

  • Model Garden — Gemini + open models in one catalog: chosen by use case, not by faith
  • Grounding — answers anchored to your data or Google Search: hallucinations down
  • RAG Engine — managed ingestion, chunking and retrieval: RAG without scaffolding
  • Pipelines — reproducible training and deploys: textbook MLOps
  • Model Monitoring — drift and skew flagged in production: the model watched
  • Provisioned throughput — reserved capacity for production: stable latency
04 · Numbers and lifecycle

The numbers that matter.

2M
context tokens on Gemini: entire archives in one prompt
100+
the models in the Garden
0
training on your data: a contractual guarantee
p95
the latency to watch per deployment: PTUs where it counts
AI in production is discipline: use cases, grounding and monitoring — we take PoCs beyond the demo.
05 · Use cases

Where it really pays off.

Internal copilots

Company knowledge queryable, with citations.

Document AI

Invoices, contracts and forms read and structured.

Agents on the processes

From information to action, within the guardrails.

Generative AI is a platform, not a subscription: Vertex puts it in order, we put it in production.