README.md

---
title: AI Inference Architecture for Healthcare
emoji: 🧠
colorFrom: indigo
colorTo: green
sdk: static
sdk_version: "1.0.0"
app_file: index.html
pinned: true
tags: [healthcare, ai-inference, mlops, kubernetes, triton-inference-server, fastapi, hipaa-compliance, deep-learning, cloud-architecture, monitoring]
description: Scalable, production-ready AI inference architecture for healthcare and pharma using Triton, FastAPI, and Kubernetes.
---
# AI Inference Architecture for Healthcare

This project provides a scalable, production-ready AI inference architecture designed for healthcare and pharmaceutical applications. It integrates Triton Inference Server, FastAPI, and Kubernetes to support high-throughput model inference.

## 🚀 Key Features

- Modular container-based architecture with FastAPI gateway
- Supports NLP and CV models with optional preprocessing
- Inference via Triton Inference Server using ONNX or TorchScript models
- GitHub Actions-powered CI/CD pipeline to auto-deploy model updates
- Kubernetes-based pod management, autoscaling, and volume mounting
- Full observability stack: Prometheus + Grafana for metrics and monitoring
- Compliant with HIPAA-aligned standards: secure APIs, logging, encryption

## 🧱 Architecture Overview

```
Healthcare/Pharma Clients → FastAPI Gateway → Optional Preprocessor → Triton Pod
       ↓                        ↓                            ↓             ↓
 Model Registry ← GitHub CI/CD Pipeline ← Kubernetes ← Monitoring (Prometheus + Grafana)
```

## ⚙️ Deployment Options

### ▶️ Local (Docker Compose)
```bash
docker compose up --build
```

### ☸️ Kubernetes (Production)
```bash
kubectl apply -f k8s.yaml
kubectl apply -f preprocessor.yaml
kubectl apply -f hpa.yaml
```

## 📦 Model Lifecycle

1. Train model locally or in pipeline (e.g., PyTorch/ONNX)
2. Push model to GitHub repository
3. GitHub Actions CI/CD triggers and pushes model to Model Registry
4. Kubernetes mounts model volume into Triton pod
5. Triton automatically reloads model

## 🔍 Monitoring and Observability

- Metrics via Prometheus sidecar scraping port 8002 on Triton pod
- Dashboards in Grafana track latency, throughput, failures

## 🧪 Sample Inference Request
```bash
curl -X POST http://localhost:8000/infer   -H "Content-Type: application/json"   -d '{"input": "Patient data or image here"}'
```

## Enhancements Based on Peer Technical Review

### Preprocessing Execution Model
The NLP/CV preprocessing stage runs as an **independent Kubernetes microservice** for isolation and scale. The FastAPI Gateway performs **conditional routing**:
- `content_type=image/*` → CV preprocessor → Triton
- `content_type=text/*` → NLP preprocessor → Triton
- Already-normalized inputs → direct to Triton
A lightweight schema-validation step remains in the gateway.

### Model Lifecycle: Versioning, Promotion, Rollback
- Models are versioned under `/models/<name>/<version>` (e.g., `/models/ner/1`).
- CI/CD publishes to **staging**; promotion updates a **release tag** (e.g., `current -> 2`) for Triton to hot-reload.
- **Rollback** re-points the tag to the last known-good (`current -> 1`).
- Supports **blue‑green** (two deployments, Service selector switch) and **canary** (small % routed to a second Triton deployment).

### Scalability & Resilience
- **HPA** scales Triton pods based on CPU (and can extend to latency custom metrics).
- **Readiness/Liveness probes** guard rollout and enable auto‑healing.
- Gateway uses timeouts and retry on transient 5xx. If a pod is Unready, traffic shifts to healthy pods.

### Security, Compliance & Audit
- **TLS in transit**; optional mTLS inside cluster.
- **OAuth2/JWT** at the gateway with per‑route scopes.
- **Audit logs** (structured JSON with `request_id`) across gateway, preprocessors, and Triton; logs ship to ELK/Loki.
- Optional **PHI de‑identification** in preprocessors; strict schema validation; data minimization and retention controls aligned to HIPAA/GDPR.

### Data Flow & Validation
- Gateway enforces **MIME/JSON schema** and rejects malformed/unauthorized requests.
- Preprocessors normalize inputs (e.g., tokenize text, resize/normalize images).
- Triton returns prediction JSON; gateway maps to a domain response schema and may **redact** fields per policy.

📌 See [SECURITY.md](./SECURITY.md) for detailed security, compliance, and audit logging implementation.

📄 See preprocessor.yaml for deployment details of the NLP/CV preprocessing microservice.

📄 See hpa.yaml for Triton autoscaling configuration.

📂 File Reference
- k8s.yaml → Triton deployment
- preprocessor.yaml → NLP/CV preprocessing service
- hpa.yaml → Horizontal Pod Autoscaler for Triton