Feedback Marko
Hi digopala,
Thanks for sharing your AI inference architecture for healthcare/life sciences. Below is technical feedback based solely on the current diagram structure.
🔍 Developer-Focused Feedback
- Unlabeled Arrows = Ambiguity in Data Flow
Arrows like Model → Triton, Triton → Healthcare Services, and Triton → Pharmaceutic Industry are missing labels.
The bidirectional arrow between “Pharmaceutic Industry” and “Kubernetes” is especially unclear. What is the intent? Model triggering? Deployment control?
📌 Recommendation: Every arrow should explicitly show the protocol or event it represents (e.g., “Load Model”, “gRPC request”, “REST response”).
- Undefined Role of Kubernetes
Kubernetes appears in the diagram but is only linked to the Model. There's no connection to Triton Inference Server or indication of workload management.
It's not clear if Triton is deployed as a pod or how scalability is handled.
📌 Recommendation: Clarify that Kubernetes is responsible for:
Deploying and scaling Triton pods
Mounting model volumes
Managing pod health and restarting on failure
- Model Lifecycle Is Not Represented
The model box simply connects to Triton and Kubernetes. There's no training pipeline, model registry, or CI/CD shown.
📌 Recommendation: Add a CI/CD or ModelOps component that pushes models to a store, enabling versioned, automated loading by Triton.
- Healthcare/Pharma as Output Targets Need Context
“Healthcare Services” and “Pharmaceutic Industry” are placed as static boxes with no protocol, UI/API interface, or directional flow.
📌 Recommendation: Represent these entities as external clients that interact via HTTP/gRPC at the top of the pipeline—not direct output sinks from Triton.
- Production Gaps
There's no observability layer: no metrics, logging, or monitoring component is shown.
No mention of authentication, request validation, or gateway between the client and inference layer.
replicas: 1 in the YAML prevents scalability and fails production expectations for availability.
📌 Recommendation:
Add a FastAPI Gateway to handle auth, request routing, and preprocessing.
Include a Monitoring Layer (e.g., Prometheus + Grafana, ELK stack) for logs and metrics.
Configure Horizontal Pod Autoscaling (HPA) in Kubernetes for scaling under load.
📩 Feel free to send me an invite on Upwork if you’d like help productionizing this architecture further - https://www.upwork.com/freelancers/~01019fb4830d509129.
Thanks,
Marko