What is Vertex AI Academy?

Vertex AI Academy is the #1 free interactive learning platform by Infinity Tech Stack for Google Cloud Vertex AI and the Gemini model family. It covers Gemini 4 Argon, Gemini 3.8 Flash, Gemini 3.8 Live, Gemini 3.6 Flash, Gemini 3.5 & 3.1 Flash-Lite, Context Caching, Grounding with Google Search, Vertex AI Agent Engine (ADK), and Remote MCP across 13 modules, 33 lessons, 33 gcloud CLI labs, and 79 quizzes with zero sign-up required.

How is Infinity Vertex AI Academy different from Google Cloud Skills Boost?

Unlike Google Cloud Skills Boost which requires GCP cloud credentials, project setup, and credits, Infinity Tech Stack's Vertex AI Academy is 100% free with 33 in-browser terminal simulations and 79 quizzes covering Gemini 4 Argon, Gemini 3.8 Flash, Context Caching, Google Search Grounding, BigQuery ML (AI.GENERATE & ML.GENERATE_TEXT), and Google ADK with no sign-up or credit card required.

How long does Vertex AI Academy take to complete?

Vertex AI Academy contains approximately 3 to 5 hours of hands-on interactive training across 13 modules and 33 lessons. Progress is saved locally in your browser so you can learn at your own pace.

Is the Vertex AI Academy free?

Yes, it is 100% free with no sign-up required. All 13 modules, 33 lessons, 33 gcloud CLI labs, and 79 quizzes are immediately accessible. Progress is saved locally in your browser.

What topics does the Vertex AI course cover?

The course covers the Vertex AI platform, Gemini 4 Argon (with 1M context and up to 1M Long Decode output), Gemini 3.8 Flash, Gemini 3.8 Live, Gemini 3.6 Flash, Gemini 3.5 & 3.1 Flash-Lite, Nano Banana 2.1, Deep Think reasoning, Implicit & Explicit Context Caching, BigQuery ML (AI.GENERATE & ML.GENERATE_TEXT), Grounding with Google Search, Model Garden (including Claude 5.x), and Vertex AI Agent Engine / ADK.

What is Context Caching in Vertex AI?

Context Caching reduces latency and input token costs by up to 90%–95% on repeated large contexts (minimum 4,096 tokens on Gemini 3.x and 4 models). Vertex AI supports automatic Implicit Caching (enabled by default on Gemini 3.x and Gemini 4 Argon with zero hourly storage fees) as well as Explicit Caching (cachedContents with configurable TTLs on Gemini 3.x models).

What is Grounding with Google Search?

Grounding connects Gemini to live Google Search results, enabling the model to provide up-to-date, factual responses with source citations - reducing hallucinations in production applications.

Do I need a Google Cloud account?

No account is needed to take the course. The built-in browser labs simulate the gcloud CLI so you can practice without a GCP account; if you want to follow along for real, a GCP free tier account is sufficient for most exercises.

Does the Vertex AI Academy include hands-on labs?

Yes. 33 hands-on terminal labs are built into the lessons - authenticate with gcloud, enable the Vertex AI API, call Gemini via REST, and run Google's Agent Development Kit using real, vendor-verified commands, with objectives, hints, solutions, and XP rewards. No sign-up needed.

What is Deep Think in Gemini?

Deep Think is Gemini's extended reasoning mode that allocates additional compute for complex multi-step problems - similar to chain-of-thought reasoning - producing more accurate results for math, coding, and scientific analysis.

The Agent Development Kit (ADK) & A2A — Vertex AI Agent Builder

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The Agent Development Kit (ADK) & A2A

🤖 Vertex AI Agent Builder 20m 350 BASE XP⌨ HANDS-ON LAB

Code-First Agent Workflows

As of June 2026, the Agent Development Kit (ADK v1.0) is the recommended way to build code-first, deterministic, and autonomous workflows in Google Cloud. It fully replaces the deprecated Vertex AI Extensions.

The ADK includes native support for the Agent-to-Agent (A2A) protocol, allowing your Vertex agents to securely discover and delegate tasks to external agents across different platforms using standardized Agent Cards.

The Interactions API

The new Interactions API (GA June 2026) provides a unified interface for managing agent session state, long-term memory, and asynchronous human-in-the-loop (HITL) handoffs, while Vertex AI Agent Engine deploys ADK agents (authored in Python, TypeScript, Go, or Java) with managed scaling:

from google.adk.agents import Agent
from vertexai import agent_engines

root_agent = Agent(
    name="enterprise_finops_agent",
    model="gemini-3.8-flash",  # Escalate complex sub-agents to "gemini-4-argon"
    instruction="Analyze BigQuery billing anomalies and recommend caching optimizations.",
    tools=[query_bigquery_spend],
)

# One-call managed deployment to Vertex AI Agent Engine
remote_agent = agent_engines.create(agent_engine=root_agent, requirements=["google-adk", "google-genai"])

⌨ HANDS-ON LABBuild & Run an ADK Agent

⭐ +200 XP

Google's Agent Development Kit is the code-first way to build agents on Vertex. Install it, run your agent in the CLI, then open the visual dev UI.

1Install the ADK Python package with pip.

2Run your agent interactively in the terminal with the adk CLI (agent folder: my_agent).

3Launch the ADK web dev UI to debug your agent visually.

lab-sandbox — simulated environment

INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$

OBJECTIVE 1 / 3 — type "hint" if stuck

SYNAPSE VERIFICATION

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What is the recommended framework for building code-first agent workflows in Vertex AI as of 2026?

Vertex AI Extensions

LangChain

Agent Development Kit (ADK)

Dialogflow CX