Why infrastructure choices matter in healthcare
A prediction that arrives after the delay has already happened isn't a prediction. It's a postmortem. Healthcare operations data also carries a level of sensitivity that consumer software doesn't have to think about. Those two constraints, speed and security, shaped almost every infrastructure decision behind LabOps AI.
AWS: the foundation
We run core hosting and storage on AWS because laboratory operations data needs infrastructure that's both auditable and elastic. A single hospital lab and a multi-site laboratory network have very different scaling needs, and AWS lets us meet both without re-architecting the platform.
- Amazon S3 & Amazon RDS: durable storage for both raw operational data and structured records
- Amazon ECS / EKS: containerized services that scale with specimen volume, not a fixed capacity plan
- AWS Lambda: event-driven processing for the alerts and notifications that need to fire the moment a condition is met
- Amazon SageMaker & Amazon Bedrock: the model training and generative layers behind prediction and the "Ask Your Lab" assistant
- Amazon CloudWatch: continuous monitoring of the platform itself, so we know about a problem before you do
Azure: machine learning and enterprise identity
Model experimentation runs on Azure Machine Learning, and several of our design partners' IT teams standardize on Microsoft Entra ID for access control, so we support it natively rather than asking hospital IT to work around us. It's a deliberate two-cloud choice, not a badge on the page.
- Azure Kubernetes Service (AKS): hosts model experimentation workloads separately from production inference
- Azure Machine Learning: experiment tracking and model versioning during development
- Microsoft Entra ID: enterprise identity and role-based access for customers who require it
- Azure Key Vault: secrets and connection strings, never stored in application code
- Azure Blob Storage: per-tenant isolated storage containers for select enterprise customers
- Azure Monitor: a second, independent monitoring signal alongside AWS CloudWatch
NVIDIA: the acceleration layer
Prediction is only useful if it's fast. NVIDIA-accelerated computing is what lets LabOps AI run inference against live operational data without introducing the kind of lag that would make an "early warning" arrive too late to matter.
- NVIDIA GPUs & CUDA: the raw compute behind model training and real-time inference
- TensorRT: optimization designed to keep prediction latency low as specimen volume scales
- Triton Inference Server: serving multiple models concurrently without a performance penalty
Security and reliability
Enterprise-grade security controls, encryption in transit and at rest, and continuous monitoring aren't optional line items for us. They're the baseline requirement for handling healthcare operations data at all. As LabOps AI scales across more laboratories, this is the layer we invest in first, not last. Full detail on how we handle data lives on our Trust & Compliance page.
AWS, Microsoft Azure, and NVIDIA are trademarks of their respective owners. LabOps AI is not affiliated with or endorsed by them.