Production-ready Jupyter Notebook with full root access, pre-configured Python environments, and security hardening - ideal for data science teams needing a private, customizable notebook server on AWS.
This fully managed Jupyter Notebook solution delivers a production-ready, secure data science platform on AWS, built on Ubuntu 24.04 LTS. Every instance comes pre-configured with security best practices, optimized Python environments, and automated operations.
Key Features
Pre-configured Data Science Environment
Jupyter Notebook comes ready with Python, pip, and essential libraries. Install TensorFlow, PyTorch, scikit-learn, or pandas as needed with full root access.
Security Hardened
HTTPS-ready configuration, token-based authentication, UFW firewall pre-configured to allow only SSH (22) and web access (80/443) from trusted sources, and latest security patches applied.
Automated Setup
On first login, an interactive setup script configures Jupyter Notebook with secure access, sets up authentication tokens, and configures firewall rules - all within minutes.
Monitoring and Integration
Integrate with AWS CloudWatch for resource monitoring. Connect to S3 for data storage, RDS for database access, or Redshift for data warehousing directly from your notebooks.
Use Cases
Data exploration, visualization, and statistical analysis
Machine learning model development and prototyping
Academic research and collaborative data science
ETL pipeline development and testing
Business intelligence reporting and ad-hoc analysis
Teaching and training environments for Python and data science
Getting Started
Launch the AMI from AWS Marketplace on your preferred instance type
SSH into your instance and follow the interactive setup wizard
Access Jupyter Notebook via your browser and start analyzing data
Support
Managed and supported by Perimattic, a cloud infrastructure company with 13+ years of experience serving global clients. We offer free setup assistance, custom integrations, infrastructure consulting, and 24/7 support.
Highlights
Full Root Access Jupyter Notebook: get complete OS-level control to install custom packages, configure GPU drivers, and manage dependencies without vendor restrictions.
Pre-configured Data Science Platform: Ready-to-use Jupyter on Ubuntu 24.04 LTS with Python and pip. Add TensorFlow, PyTorch, or any framework with full root access.
24/7 Expert Support by Perimattic: 13+ years of cloud infrastructure expertise with free setup assistance, environment configuration, and dedicated support at aws-support@perimattic.com.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
If you are an AWS Free Tier customer with a free plan, you are eligible to subscribe to this offer. You can use free credits to cover the cost of eligible AWS infrastructure. See AWS Free Tier for more details. If you created an AWS account before July 15th, 2025, and qualify for the Legacy AWS Free Tier, Amazon EC2 charges for Micro instances are free for up to 750 hours per month. See Legacy AWS Free Tier for more details.
You pay by the hour for the managed Jupyter notebook platform, billed per running instance. Each dimension maps to a specific AWS EC2 instance type, so your rate depends on the compute class you pick. Options span general-purpose (m, t), compute-optimized (c), memory-optimized (r, x, z), storage-optimized (d, i), accelerated GPU and inference families (p, g, inf, trn), and high-memory (u) instances. Larger sizes within a family cost more per hour, matching added CPU, memory, or accelerators. You run only the instances you choose and pay for the hours they run.
Top-of-mind questions for buyers
What does one hourly unit map to in concrete terms?
Each unit is one running managed Jupyter notebook instance on a specific AWS EC2 instance type. You pay the hourly rate for the compute class you select, such as c5.large or g5.xlarge. The rate reflects that instance's CPU, memory, and accelerator capacity. Each running instance meters separately.
Am I charged when a notebook instance is stopped or idle?
The software charge meters running hours only. A fully stopped instance stops accruing the hourly software charge. Stopped instances may still incur underlying AWS fees for attached storage, but the managed platform charge applies only while the instance runs.
What does the managed service handle beyond the hourly compute charge?
The vendor provisions, patches, backs up, and monitors each instance so you avoid separate DevOps work. Deployment, security updates, and daily backups are part of the managed offering. The hourly rate you pick reflects the compute class, while the operational baseline stays consistent across instance choices.
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Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA).
Content disclaimer
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An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Initial release of a fully automated Jupyter Notebook environment for AWS EC2.
Includes official Jupyter Data Science Notebook with preinstalled Python libraries.
Secure first-boot configuration with auto-generated strong password.
Persistent notebook and data storage preconfigured using Docker volumes.
Domain mapping wizard with DNS validation during first setup run.
Automated SSL certificate provisioning with Certbot for secure HTTPS access.
Integrated Apache reverse proxy for hardened access to Jupyter over the internet.
Automatic logging and configuration stored in /opt/app/.env for easy credential recovery.
Guided onboarding with interactive setup scripts and failsafe rerun support.
Additional details
Usage instructions
Launch an Instance
From AWS Marketplace, click Continue to Subscribe, then Continue to Configuration, and launch the AMI.
Choose the Recommended Instance Type
Select t2.medium or higher for a smooth Jupyter Notebook experience.
Data science workloads benefit from more CPU/RAM - larger instance types will significantly improve notebook execution performance and library handling.
Configure Security Group
Allow inbound traffic on the following ports:
22 (SSH) - For secure terminal access
80 (HTTP) - Required temporarily for SSL validation
443 (HTTPS) - Secure access to Jupyter Notebook in browser
Connect to the Instance
Use SSH to connect:
ssh -i <your-key.pem> ubuntu@<public-ip>
First-Boot Setup (Guided)
On first boot, an interactive guided setup will automatically start:
Detects public IP
Prompts for the domain name to map Jupyter Notebook
Validates DNS A-record propagation
Asks for admin email for SSL certificate issuance
Generates a secure random Jupyter access password
Stores credentials securely in /opt/app/.env
Configures Apache reverse proxy for HTTPS
Requests and installs a free Let's Encrypt SSL certificate
Deploys Jupyter Notebook using Docker Compose
Shows access URL and generated password after completion
DNS Configuration Required
Before (or during) the setup script, add this DNS A-record:
yourdomain.com <your-instance-public-IP>
Setup continues only when global DNS resolvers confirm valid mapping.
Automatic SSL Setup
After DNS validation completes:
Enables HTTPS access to Jupyter Notebook
Auto-installs valid TLS certificate from Let's Encrypt
HTTP access is disabled for security
Start / Restart Jupyter Manually (if needed)
cd /opt/app
docker compose ps
docker compose restart
Access the Jupyter Notebook
Open your browser and visit:
https://<your-domain>
Login credentials (displayed after setup & stored in .env):
Username: root
Password: <generated-password>
Retrieve Credentials Anytime
cat /opt/app/.env
Thank you for subscribing. Your instance is ready to go!
Need Support or Customization?
Whether you're facing setup issues or need custom features, Perimattic's expert team is here to help:
We offer:
Free setup assistance
Custom development & integrations
Infrastructure consulting
Automation & AI solutions
With 13+ years of experience serving global clients, we ensure your deployment runs smoothly and scales with your needs.
What We Help With:
Initial setup and configuration assistance (free)
Troubleshooting connectivity, performance, and backup issues
Custom development and integrations
Infrastructure consulting and architecture review
Migration planning from other solutions
Automation solutions and scaling guidance
Instance Sizing Guidance:
t2.medium or t3.medium: Development environments, small applications
m5.large: Production workloads with moderate traffic
m5.xlarge: High-traffic production deployments and larger datasets
For workload-specific sizing recommendations, contact our team for a free consultation.
Refunds and Issues:
If you experience any issues or need to request a refund, contact us at aws-support@perimattic.com and we will respond promptly.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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