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 3.8 Flash, Gemini 3.5 Flash-Lite, Context Caching, Grounding with Google Search, Managed Agents, and Remote MCP across 13 modules, 33 lessons, and 54+ quizzes with hands-on gcloud labs and 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 in-browser terminal simulations covering Gemini 3.8 Flash, Context Caching, Google Search Grounding, BigQuery ML, 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 2 to 3 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, lessons, and 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 3.8 Flash, Gemini 3.5 Flash-Lite, Gemini 2.5 Pro, Deep Think reasoning, 1M+ token Context Windows, Context Caching, BigQuery ML, Grounding with Google Search, Vertex AI Studio, and Agent Builder.

What is Context Caching in Vertex AI?

Context Caching lets you store large context payloads (documents, system prompts) server-side and reuse them across multiple requests, reducing latency and cost by up to 75% for repetitive workloads.

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. 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.

How do you enable code execution in a Gemini API call?

Pass a Tool.from_code_execution() in the tools parameter — Code execution is enabled by passing Tool.from_code_execution() in the tools parameter of your GenerativeModel or generate_content call.

Why is built-in code execution better than the model doing mental math?

The model executes actual Python to verify calculations, eliminating arithmetic errors from token-by-token generation — LLMs can make arithmetic errors when computing token-by-token. Code execution lets the model write and run real Python, ensuring mathematical correctness.

Built-in Code Execution Tutorial | Code Execution & Live API

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Built-in Code Execution

💻 Code Execution & Live API 12m 250 BASE XP⌨ HANDS-ON LAB

Sandboxed Python in Gemini

Gemini models include a built-in code execution capability that runs Python code in a secure, sandboxed environment. Unlike external code interpreters, this runs directly within the Gemini API call - the model writes code, executes it, and uses the results to formulate its final answer.

What's Available in the Sandbox

Python 3.x runtime with standard library
NumPy for numerical computing
Pandas for data manipulation
Matplotlib for chart generation

Enabling Code Execution

Enable it by passing a code_execution tool in your request:

from google.genai import GenerativeModel, Tool

model = GenerativeModel(
    "gemini-3.8-flash",
    tools=[Tool.from_code_execution()]
)

# The model will write and execute Python to solve this
response = model.generate_content(
    "Calculate the first 20 Fibonacci numbers and plot them as a line chart."
)
# Response includes the generated chart and computed values

Use Cases

Task	How Code Execution Helps
Math Verification	Model writes Python to verify calculations instead of relying on mental math
Data Analysis	Process uploaded CSVs with Pandas, generate statistics
Chart Generation	Create matplotlib visualizations inline
Algorithm Testing	Write and run code to test algorithmic solutions

⚡ Key Advantage: Built-in code execution is serverless and requires no infrastructure setup. Just add the tool to your request and Gemini handles the rest.

⌨ HANDS-ON LABLet Gemini Run Python on Google's Sandbox

⭐ +150 XP

Math by prediction is a coin flip. Attach the codeExecution tool so Gemini writes and actually runs Python inside the API call, then returns verified results.

1Capture an access token in the TOKEN variable.

2POST to generateContent with "tools":[{"codeExecution":{}}] and ask for a verifiable computation.

lab-sandbox — simulated environment

INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
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OBJECTIVE 1 / 2 — type "hint" if stuck

SYNAPSE VERIFICATION

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What libraries are available in Gemini's built-in code execution sandbox?

Only the Python standard library

Python standard library plus NumPy, Pandas, and Matplotlib

Every pip package

Only TensorFlow