Enabling AWS GenAI Tools SageMaker AI and Bedrock with Research Gateway
In the fast-evolving world of scientific discovery, researchers need access to powerful Generative AI capabilities — but setting up and managing AI environments in the AWS console can be complex and time-consuming.
Watch how Relevance Lab’s Research Gateway enables researchers to easily build, train, and deploy intelligent AI agents using AWS SageMaker and Amazon Bedrock — without needing deep cloud expertise.
How It Works
Watch how a research team can set up, build, and deploy an AI Agent for their project in just a few steps:
Step 1 – Set Up the Research Domain: The Principal Investigator (PI) creates a new project in Research Gateway, provisioning an Amazon SageMaker AI Domain in just a few clicks — no manual AWS console setup needed.
Step 2 – Launch JupyterLab in SageMaker: Researchers log in through Research Gateway and open JupyterLab directly within SageMaker for model training, coding, and experimentation.
Step 3 – Prepare the Data: Upload domain-specific datasets (e.g., journals in Amazon S3) that power Retrieval-Augmented Generation (RAG) for context-aware responses.
Step 4 – Design the AI Agent Use Case: Using LangChain, Prompt Engineering, and Streamlit within SageMaker, researchers can design AI agents that define logic, integrate knowledge bases, and provide real-time chat-style answers from uploaded research papers without any DevOps setup.
Step 5 – Build the AI Agent: Define your agent’s core purpose — from content aggregation to interactive research Q&A and automated summarization.
Step 6 – Test & Fine-Tune: Iteratively test, refine, and validate the AI’s outputs to align with scientific goals and ensure precision.
Step 7 – Cost & Governance: Research Gateway provides real-time cost visibility, responsible AI guardrails, and security compliance, enabling research teams to leverage AWS GenAI with transparency, control, and confidence.
Why Research Gateway + AWS GenAI
- Simplifies access to SageMaker and Bedrock
- Enables faster experimentation and deployment
- Delivers cost governance and security by design
- Empowers teams to focus on science, not infrastructure
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