Amazon SageMaker Studio Lab: Hands-on Guide (2026)

SageMaker Studio Lab is a free, no-AWS-account-required environment for machine learning. As of August 2025, it runs JupyterLab 4. This brief tutorial gets you from zero to running models in minutes.

What You Get (Free)

• Compute: CPU or GPU (e.g., NVIDIA T4)
• Storage: 15GB persistent
• Environment: Pre-configured Python with popular ML libraries
• Session limits: 12 hours (continuous), then auto-reset

Not for production – No deployment, pipelines, or real-time inference. Perfect for learning, prototyping, and teaching.

Step 1: Sign Up (No AWS Account Required)

1. Go to https://studiolab.sagemaker.aws
2. Create an account with your email
3. Verify your email – done!

Step 2: Create Your First Project

1. Click Projects → New Project → name it (e.g., "my-first-ml")
2. Choose CPU (general) or GPU (deep learning)
3. Click Start Runtime → Open Project

You'll see JupyterLab 4 – familiar notebook interface with file browser, terminal, and extension manager.

Step 3: Install Packages (If Needed)

Studio Lab comes with numpy, pandas, scikit-learn, torch, tensorflow. To add more:

# Open a terminal inside JupyterLab
pip install transformers datasets

For persistent installs (survives restart), use:

pip install --user your-package

Step 4: Train a Minimal Model (Copy-Paste to Run)

Create a new notebook (File → New → Notebook). Paste and run:

# Basic ML example
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

X, y = load_iris(return_X_y=True)

clf = RandomForestClassifier()
clf.fit(X, y)

print(f"Training accuracy: {accuracy_score(y, clf.predict(X)):.3f}")

Step 5: Use the GPU

1. Stop runtime
2. Edit Project → Change to GPU
3. Start runtime again

import torch
print(torch.cuda.is_available())

Step 6: Save & Share Your Work

• Save files normally – persistent storage
• Export project as .tar.gz
• Download notebooks as .ipynb

Pro Tips for 2026

• Session resets after 12h – save frequently
• Use --user installs for persistence
• Clone GitHub repos using terminal
• Move to SageMaker for production

How It Compares to Colab

Feature	SageMaker Studio Lab	Google Colab
Free GPU	Yes (T4)	Yes
Persistent storage	Yes (15GB)	No
Session length	12h	~12h

What’s Missing vs. Full SageMaker

• No deployment endpoints
• No pipelines
• No Data Wrangler

Next Steps After This Tutorial

1. Build computer vision models
2. Use Hugging Face transformers
3. Move to full SageMaker

Prepare for AWS Certifications

• AWS Cloud Practitioner Practice Tests
• AWS Developer Associate Practice Tests
• AWS Machine Learning Specialty Practice Tests
• AWS Generative AI Developer Practice Tests

Frequently Asked Questions (FAQs)

Is Amazon SageMaker Studio Lab really free and do I need an AWS account?

Yes, SageMaker Studio Lab is completely free and does not require an AWS account, credit card, or subscription. You can sign up using just your email and start using CPU, GPU, and persistent storage for machine learning.

Does SageMaker Studio Lab provide GPU access?

Yes, Studio Lab provides GPU instances such as NVIDIA T4 for deep learning workloads. You can switch between CPU and GPU directly from the project settings.

How long can I run a session and what happens after it ends?

Each session can run for up to 12 hours continuously. After that, the runtime resets automatically. However, all your files remain محفوظ in persistent storage, and you can restart the session and continue working.

Can I install additional Python libraries and keep them after restart?

Yes, you can install additional libraries using pip. To make installations persistent across sessions, use the --user flag while installing packages.

Is SageMaker Studio Lab suitable for production use?

No, Studio Lab is designed for learning, prototyping, and experimentation. For production use cases such as deployment, pipelines, and real-time inference, you should use full Amazon SageMaker.

How does SageMaker Studio Lab compare to Google Colab?

Studio Lab provides persistent storage and more stable sessions, which gives it an advantage over Google Colab. However, both platforms are useful depending on your use case and workflow requirements.

Which AWS certification should I take after learning SageMaker Studio Lab?

You can start with foundational certifications like AWS Cloud Practitioner and then move to advanced certifications such as Developer Associate or Machine Learning Specialty.

AWS Cloud Practitioner Practice Tests

Where can I practice AWS certification exam questions?

You can use MyExamCloud practice tests that include real exam-level questions, mock exams, and detailed explanations.

Can I export my work from SageMaker Studio Lab?

Yes, you can export your entire project as a .tar.gz file or download individual notebooks as .ipynb files.

What should I learn after this tutorial?

After completing this tutorial, you can explore deep learning, natural language processing using transformers, and eventually move to full Amazon SageMaker for production workflows.