OtoSage AWS
AWS-native healthcare AI platform architecture, deployment controls and browser-side evidence workbench.
Author: Ankit Kumar Singh
Evidence boundary: this public Space does not contact AWS or claim an executed SageMaker endpoint.
Live image intake QA
Select a permitted image. The browser computes dimensions and brightness locally, representing the intake-quality gate before a cloud inference request.
Choose an image to inspect the client-side intake signal.
Event-driven AWS path
Live now
Browser intake QA
Architecture explorer
Hugging Face CI publication
Implemented in GitHub
SageMaker pipeline design
Model Registry + quality gates
Async inference pattern
Terraform + OIDC
Not overstated
No live AWS endpoint claimed
No cloud latency/cost claimed
No clinical validation claimed
Why this matters for AI platform leadership
The project separates model quality, approval, infrastructure and runtime concerns. That makes the release path auditable and demonstrates platform-level thinking beyond a notebook-only model.