Sold by: Madarson IT
Deployed on AWS
AWS Free Tier
This product has charges associated with it for AI/Machine Learning Env. Red Hat Enterprise Linux-based AI/ML environment featuring JupyterLab, Python, and common data science libraries. Designed for secure, persistent workloads on Azure with enterprise-grade defaults and token-based access.
Overview
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Secure JupyterLab Access via Token Authentication
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Preconfigured JupyterLab Environment
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Integrated Notebook and Development Tools
This is a repackaged software product from Madarson IT with additional charges applied for AI/Machine Learning Env. This Azure-based virtual machine image provides a secure, enterprise-ready AI and machine learning environment built on Red Hat Enterprise Linux (RHEL). It includes JupyterLab pre-installed and configured with token-based authentication.
Key Features:
- Red Hat Enterprise Linux optimized for Azure
- JupyterLab in a Python virtual environment
- Token-based authentication enabled by default
- Persistent storage aligned with Azure best practices
- SELinux enforcing and firewall enabled
Network Access: JupyterLab listens on TCP port 8888. Customers must explicitly allow inbound access to this port using an Azure Network Security Group or equivalent firewall configuration. No ports are exposed automatically.
Enterprise-Ready Design: Application binaries and user notebooks are stored on persistent disks to ensure durability across reboots and redeployments. Azure ephemeral storage is not used for application data.
Security Model:
- No hardcoded credentials or embedded secrets
- Runtime-generated access tokens
- SELinux enforcing mode
- Minimal exposed surface area
Typical Use Cases:
- Data science and machine learning experimentation
- Model prototyping
- Enterprise AI/ML proof-of-concept environments
- Training and educational labs
Getting Started:
- Deploy the VM from Azure Marketplace
- Connect via SSH
- Allow TCP port 8888
- Start JupyterLab
- Access via browser using token
Why Choose Madarson IT Images? Madarson IT certified images are continuously updated, security-optimized, and built to meet industry requirements with minimal configuration. They are tested, deployment-ready, and ideal for secure cloud workloads. For private offers, custom security requirements, or compliance needs, contact info@madarsonit.com .
Disclaimer: Red Hat, Inc holds the trademarks for Red Hat Enterprise Linux (RHEL), and its associated branding. Madarson IT does not provide commercial licenses on any product.
Highlights
- . Optimized Python AI/ML environment with JupyterLab, NumPy, Pandas, scikit-learn, XGBoost, LightGBM, and visualization libraries preinstalled . Persistent notebook and environment storage designed for cloud images (no dependency on ephemeral disks)
- . Systemd-managed JupyterLab service for automatic startup, reliability, and simplified operations . Ideal for quick-start AI/ML labs, PoCs, and training environments on public cloud infrastructure
- . Token-based authentication enabled by default (no hardcoded credentials or passwords) . Customer-controlled network exposure, requiring explicit opening of TCP port 8888 via cloud security groups
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Rhel 10
Deployed on AWS
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Pricing
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.
Dimension | Cost/hour |
|---|---|
m5a.xlarge Recommended | $0.20 |
t2.micro | $0.05 |
t3.micro | $0.10 |
d3.xlarge | $0.20 |
c5a.xlarge | $0.20 |
g5.xlarge | $0.20 |
t3.nano | $0.05 |
t3a.nano | $0.05 |
m5a.2xlarge | $0.40 |
m6a.4xlarge | $0.80 |
Vendor refund policy
There is no refund policy for this image.
Legal
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
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
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
Red Hat Enterprise Linux 10 AI/ML Environment with JupyterLab
Additional details
Usage instructions
Allow inbound SSH access in your security group (TCP port 22) Allow inbound Jupyter web access in your security group on TCP port 8888 To connect to your instance using the Amazon EC2 console: Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/ . In the navigation pane, choose Instances. Select the instance and choose Connect. Choose the EC2 Instance Connect tab. For Connection type, choose Connect using EC2 Instance Connect. Access the ec2 with the default username: "ec2-user" OR Alternatively, access Jupyter web console at http://your-vm-ip:8888 To Get access URL / token from the terminal: sudo journalctl -u jupyterlab | grep token
Resources
Vendor resources
Support
Vendor support
To speak with us about private offers, audit or your compliance needs, please contact us at info@madarsonit.com .
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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