Relevance Lab Data Science Workspace for Amazon Linux

The Relevance Lab Data Science Workspace is a cloud-based, ready-to-use Amazon Machine Image (AMI) built on Amazon Linux 2023. It eliminates hours of manual environment setup by delivering a fully configured virtual desktop with 15+ pre-installed tools for statistical computing, machine learning, and software development. Data scientists, researchers, and analysts can launch the AMI and begin productive work in under 5 minutes.

Unlike the AWS Deep Learning AMI (which focuses on Python ML frameworks without RStudio or office productivity tools), or SageMaker notebooks (which lack a full desktop environment with Docker and multiple IDEs), this workspace combines a complete R-focused analytics stack, Python ML frameworks, containerization tools, and productivity applications in a single, tested image.

Compared to manually assembling a Docker-based environment or installing tools one by one, this AMI eliminates dependency conflicts and version mismatches. Every framework is pre-validated to work together - PyTorch, TensorFlow, scikit-learn, PySpark, and Vowpal Wabbit coexist without library collisions.

Built by Relevance Lab - a team with deep expertise in cloud workspace automation - this AMI is designed for teams that need a validated, production-grade data science environment without the overhead of building and maintaining one from scratch.

• RStudio Server 2024.12.0 with R and CRAN access
• JupyterLab with Python and R kernels
• PyTorch, TensorFlow, scikit-learn, PySpark, Vowpal Wabbit - pre-configured ML frameworks
• Visual Studio Code and PyCharm Community Edition - development IDEs
• Docker and Docker Compose - containerization tools
• Anaconda - Python and R package management
• LibreOffice Suite - document authoring and spreadsheets
• NVIDIA GPU drivers - pre-installed for accelerated computing on supported instances

For a complete tool-version compatibility matrix and deployment guide, see the Additional Resources links on this listing page.

Immediate Productivity with RStudio Server - Launch RStudio Server with R and essential libraries pre-installed. Access your analytics environment through a browser without local installation. Additional packages are available via CRAN.

Interactive Notebooks with JupyterLab - Pre-installed JupyterLab with Python and R kernels enables interactive data exploration, visualization, and collaborative analysis from day one.

Full ML/AI Toolkit - PyTorch, TensorFlow, scikit-learn, PySpark, and Vowpal Wabbit are pre-configured for end-to-end machine learning workflows - from data preparation through model training and evaluation.

Multiple Development IDEs - Visual Studio Code and PyCharm Community Edition provide lightweight, flexible coding environments for Python, R, and general-purpose development.

Containerized Workloads - Docker and Docker Compose enable reproducible model deployments and portable data pipelines without additional installation.

Python and R Package Management - Anaconda is pre-installed for managing isolated environments, preventing package conflicts across projects.

Document Authoring - LibreOffice Suite supports report creation, spreadsheet analysis, and presentations without leaving the workspace.

The AMI is built on Amazon Linux 2023 with the latest security patches applied at publish time. RStudio Server access is password-protected and accessible via browser over configurable ports. SSH key-based authentication is supported for instance access. We recommend configuring security groups to restrict inbound traffic to only the required ports (8787 for RStudio Server, 8888 for JupyterLab) and trusted IP ranges. Docker runs with standard Linux permissions, and Anaconda environments provide process-level isolation between projects. Buyers should follow AWS best practices for VPC configuration, encryption of EBS volumes at rest, and IAM role assignment for accessing S3 or other AWS services.

Genomics and Life Sciences Research - Research teams working with large-scale sequencing datasets can launch this workspace on GPU-backed instances, use JupyterLab to preprocess and visualize genomic data, train classification models with PyTorch or TensorFlow, and package validated pipelines into Docker containers for reproducible analysis.

Financial Risk Modeling - Quantitative analysts can leverage RStudio Server for statistical modeling on time-series market data, use scikit-learn and PySpark for feature engineering across millions of transaction records, and produce compliance-ready reports in LibreOffice - all within a single auditable environment.