RelevanceLab Generative AI & ML Server - Preconfigured LLM Stack

Building a production-ready Generative AI environment from scratch means hours of dependency resolution, version-conflict debugging, and manual AWS service integration. This preconfigured Amazon Linux 2023 AMI eliminates that complexity by delivering 20+ pre-tested AI/ML frameworks, AWS-native connectors, and runnable sample workflows in a single image - ready to use within minutes of launch.

Unlike vanilla Deep Learning AMIs or manual pip-install approaches, this stack uniquely combines RAG-ready vector search (FAISS), medical imaging (MONAI), LLM fine-tuning (LoRA/PEFT), and pre-wired Bedrock and SageMaker integrations that no single AWS-managed AMI provides.

• Researchers save iteration time with JupyterLab, RStudio, and pre-loaded sample notebooks
• Enterprise teams reduce onboarding friction with a verified, consistent environment across team members
• Developers accelerate prototyping with Docker-ready microservices and pre-configured IDE tooling

Generative AI - Ready in Minutes

• Hugging Face Transformers, LangChain, and LlamaIndex pre-installed and verified compatible
• FAISS configured for vector search and RAG pipeline development
• LoRA and PEFT frameworks for efficient LLM fine-tuning on GPU instances
• Sample RAG chatbot workflow using Bedrock + OpenSearch included as a runnable notebook

AWS-Native Integrations

• Bedrock SDK pre-configured for foundation model access
• SageMaker SDK ready for training and deployment workflows
• OpenSearch connectors for building scalable retrieval-augmented generation applications

Full Development Stack

• Visual Studio Code, PyCharm Community Edition, JupyterLab, and RStudio Desktop and Server
• PyTorch, TensorFlow, scikit-learn, PySpark, Dask, and Vowpal Wabbit
• Docker and Docker Compose for containerized deployments
• Anaconda environment management for reproducible experiments

High-Performance Remote Access

• Amazon NICE DCV delivers encrypted, low-latency remote desktop access
• Google Chrome, Git, and AWS CLI preinstalled for immediate productivity
• LibreOffice for document editing and reporting

Sample Workflows Included

• RAG chatbot with Bedrock + OpenSearch
• Multimodal AI pipelines
• MONAI medical imaging use cases

1. Subscribe to the AMI and select your instance (GPU: g5, p4d, p5 for LLM fine-tuning; CPU for lighter tasks)
2. Configure your VPC and security groups to restrict inbound access to necessary ports
3. Launch the instance and connect via Amazon NICE DCV remote desktop
4. Open JupyterLab and run the included RAG chatbot notebook to validate your environment

For Bedrock and SageMaker workflows, ensure your IAM role has the appropriate service permissions attached.

Software charges apply per hour on top of standard EC2 infrastructure costs. See the Pricing tab on this page for detailed rate information by instance type. To evaluate the AMI at minimal cost, launch on a smaller CPU instance to explore the environment and sample notebooks before scaling to GPU instances for production workloads.

Amazon NICE DCV connections use TLS encryption for secure remote access. We recommend launching this AMI within a properly configured VPC with security groups restricting inbound access to necessary ports only. For GPU workloads involving sensitive data, enable EBS encryption on attached volumes.

• Operating System: Amazon Linux 2023
• Languages: Python 3.x, R
• Recommended Instances: GPU instances (g5, p4d, p5 families) for LLM fine-tuning, PyTorch, and TensorFlow workloads. CPU instances suitable for data preparation, LangChain development, and lighter ML tasks.
• Remote Access: Amazon NICE DCV
• Containerization: Docker, Docker Compose, Anaconda

RelevanceLab builds cloud-native solutions that accelerate AI adoption on AWS. The team specializes in preconfigured AI/ML environments designed for rapid deployment on AWS infrastructure. For a guided walkthrough or pilot consultation, contact the team through the support channel listed on this page.