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Empowering Life Sciences Research with the Insight Generation Research Platform and Cloud HPC
The Challenge
A global biopharmaceutical research company wanted to build a stronger base for its research computing to match how quickly their teams were growing into AI and ML driven work. Their scientists were going deeper into digital biology, and the workloads were getting larger and more complex every month.
The organisation wanted to bring all of this into one connected setup that could scale easily, stay stable, and still let their researchers experiment freely. They wanted a setup that was automated and dependable but also simple enough to use day to day.
- Data Silos
Different research teams were running their own clusters which made it tough to share or compare results. The idea was to bring all of them together under one system so that collaboration becomes natural and not forced.
- Expanding Workload Demands
As the research grew, the amount of data and simulations increased fast. Biologics discovery and CryoEM analysis both needed stronger GPU performance and faster turnaround to keep up with their speed of work.
- Operational Streamlining
A lot of time was spent managing and setting up the environments by hand. The company wanted to move this into automation so their teams could spend that time on research, not repetitive setup.
- Consistency and Standardisation
Each environment was being built a little differently, depending on who handled it. They needed one standard way to deploy, monitor, and maintain everything without redoing the setup each time.
This entire shift was meant to give scientists a system that grows with them — one that supports experiments as they happen, without slowing anything down.
Our Solution
The Insight Generation Research Platform team partnered with our engineering group to create a fully automated cloud setup that powers research at scale. The whole platform runs on AWS Parallel Clusters with NVIDIA GPUs and is managed through infrastructure as code. Every environment behaves the same way, scales the same way, and is easy to maintain even as new workloads come in.
Infrastructure as Code Automation
Terraform and Ansible were used to set up and manage the clusters on AWS, so every project gets a consistent, ready to go environment.
GPU Optimised Workloads
AWS Parallel Clusters with NVIDIA GPUs now handle the biologics discovery and CryoEM workloads, giving scientists all the computing strength they need for complex modelling.
Reproducible Environments
Automated AMI pipelines make sure every compute image stays consistent, keeping results accurate and experiments repeatable every time.
End to End Monitoring
Dynatrace, Grafana and Prometheus give full visibility across performance, costs, and cluster activity so teams can plan, respond, and enable insights generation in real time.
Operational Support
The team provides daily management and platform support so research continues smoothly without anyone worrying about the systems in the background.
The Outcomes
The Insight Generation Research Platform changed how their research happens. The environment is now flexible, automated and ready for AI and ML workloads, giving the teams faster access to computing power and more time for experiments. This setup also supports faster insights generation across research activity, letting teams view performance, data trends, and results in near real time.
Continuous Innovation Enablement
New clusters and workloads can be added whenever needed without interrupting what’s already running.
Operational Efficiency and Flexibility
Automation has reduced manual setup and brought more stability, helping teams respond faster to research needs.
Empowered Research Teams
Scientists now work knowing the infrastructure will handle whatever scale they need, so they can focus completely on discovery.
Future Ready Scalability
As research programs expand, the system keeps growing right along with them, making sure computing never slows down innovation.
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