Jupyter Notebook Lab

Overview

The Jupyter Notebook Lab is your starting environment at AI Labs. Built on Google Cloud's Vertex AI Workbench, every student gets a fully managed JupyterLab instance that launches in under 60 seconds. No local installs, no dependency conflicts, no "it works on my machine" — just open your browser and start coding. This lab is used across all beginner and intermediate courses for Python programming, data exploration, statistical analysis, and introductory machine learning. It comes pre-loaded with all the libraries you need so you can focus on learning, not environment setup.

What You'll Do in This Lab

Write and execute Python code in interactive Jupyter notebooks
Explore and visualize datasets with Pandas, Matplotlib, and Seaborn
Train and evaluate ML models with scikit-learn
Version your notebooks and code with built-in Git integration
Access shared datasets stored on Google Cloud Storage
Submit completed exercise notebooks for automated grading

Lab Workflow

1

Launch

Click "Start Lab" from your course dashboard. A Vertex AI Workbench instance is provisioned for you within 60 seconds with all dependencies pre-installed.

2

Code

Open JupyterLab in your browser. Each exercise has a starter notebook with instructions, code cells to fill in, and test cells that verify your work.

3

Data Access

Datasets are pre-loaded in the /data directory of your instance, synced from Cloud Storage. No downloads or uploads needed.

4

Save & Submit

Your work auto-saves to persistent disk. When you're done, run the submission cell to push results for grading and feedback.

5

Shutdown

Labs auto-shutdown after 30 minutes of inactivity to optimize costs. Your files persist and will be there when you return.

Hardware & Environment

Machine Type	n1-standard-4 (4 vCPU, 15 GB RAM)
Storage	100 GB Standard persistent disk
GPU	None (CPU-only environment)
OS	Debian 11 with Python 3.11
Idle Timeout	30 minutes auto-shutdown
Network	VPC with access to Cloud Storage, BigQuery, and Vertex AI APIs

Frequently asked questions about this lab

What is the Jupyter Notebook Lab? +

Standard Python development environment for data exploration, visualization, and introductory ML. Pre-configured with Python 3.11, NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn.

Which courses use this lab? +

This lab is included in: Python & AI Essentials: Code Your Way into Intelligent Systems, Machine Learning Engineering: Build, Optimize & Deploy Intelligent Models, Deep Learning Mastery: Neural Architectures to Real-World Applications, Generative AI & Prompt Engineering, Data Engineering for AI, AI for Cybersecurity.

What hardware does this lab run on? +

Vertex AI Workbench. Machine Type: n1-standard-4 (4 vCPU, 15 GB RAM); Storage: 100 GB Standard persistent disk; GPU: None (CPU-only environment); OS: Debian 11 with Python 3.11.

What software comes pre-installed? +

Comes pre-loaded with JupyterLab, Python 3.11, NumPy / Pandas / Matplotlib, scikit-learn, Git integration. No local installs or dependency setup required — open your browser and start working.

What happens when I am idle in the lab? +

30 minutes auto-shutdown. Your files and notebooks persist on disk and will be available the next time you launch the lab.

Can I bring my own datasets and code into this lab? +

Yes. Datasets can be uploaded directly or synced from Google Cloud Storage. Notebooks and source files have built-in Git integration so you can push work to your own GitHub or GitLab repos.

Do I need to enroll in a course to use this lab? +

Yes. Lab environments are provisioned per-student as part of an AI Labs course enrollment. Browse the courses linked above to find programs that include this lab.

Related labs

Other AI Labs environments students typically use alongside this one.

Ready to Try This Lab?

Enroll in a course that uses this lab, or visit our Houston center for a hands-on demo.

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