European University Cuts AI Research Costs with Cloud ML Platform
Executive Summary
Using Amazon SageMaker, a European agricultural university cut GenAI/LLM dev costs by 50% and accelerated model training by 80%. The cloud environment enables researchers to build and fine-tune custom foundation models for environmental science applications, speeding up innovation.
Customer Background
This established academic research institution focuses on interdisciplinary research and education in agriculture, biotechnology, and sustainable development. The university needed to modernize its machine learning infrastructure to support researchers and students developing AI models for environmental and agricultural applications.
Challenge
Before implementation, academic staff developed machine learning models on local computers, creating significant limitations in performance, security, and scalability. Researchers lacked access to high-performance GPU infrastructure required for fine-tuning large language models and working with foundation models. The university needed a centralized platform that would enable GenAI experimentation, remote access, standardized research infrastructure, and facilitate collaboration across scientific disciplines while reducing GPU-related costs.
Solution
Trek2Summit implemented a cloud-based GenAI research platform using Amazon SageMaker to provide on-demand Jupyter Notebook environments with preconfigured libraries for LLM development and foundation model fine-tuning. The solution includes:
- Amazon SageMaker - Core platform for managing GPU-optimized instances (G4/G5, P3) enabling foundation model fine-tuning, including Hugging Face Transformers, PyTorch, and LangChain
- Amazon Bedrock Integration - Access to Claude and Titan models for prompt engineering and RAG experimentation
- Amazon S3 - Centralized storage for training datasets (agricultural sensor data, emission measurements), model checkpoints, and evaluation results
- IAM Role Management - Role-based access control separating permissions for users and research groups
- On-Demand Launch Portal - Web-based interface enabling researchers to start notebook instances
- AWS Budgets - Cost monitoring and management for experimentation control
The architecture supports GenAI workflows including LoRA fine-tuning of LLMs on domain-specific agricultural datasets, enabling researchers to adapt foundation models for environmental science applications with minimal operational overhead.
Results
- 50% cost reduction compared to previous on-premise setups
- 80% faster model training times, enabling quicker experimentation cycles
- Simplified research workflows with cloud-hosted Jupyter environments
- Scalable platform ready for expansion to multiple researchers and hundreds of students
- Research example applications include environmental impact studies of agricultural emissions
AWS Services UsedAmazon SageMaker | Amazon S3 | AWS IAM | AWS Budgets