Distributed AI Inference & Coordination Platform

A production-grade distributed backend system for serving ML models at scale with real-time and batch inference capabilities.

🎯 Project Overview

This platform demonstrates advanced backend engineering, distributed systems design, and MLOps practices. It's designed to showcase skills directly relevant to senior backend roles requiring Go, Kubernetes, and AI infrastructure expertise.

This is infrastructure engineering, not an AI/ML research project.

Key Capabilities

• 🚀 Scalable Microservices - Go-based services with gRPC and REST APIs
• ⚡ Real-time & Batch Inference - Support for both synchronous and asynchronous workloads
• 🔄 Intelligent Routing - Model versioning, canary deployments, and latency-based routing
• 📊 Production Observability - Prometheus metrics, OpenTelemetry tracing, structured logging
• 🛡️ Fault Tolerance - Circuit breakers, retries, graceful degradation
• ☸️ Kubernetes Native - Full K8s deployment with HPA and service mesh ready
• 🔐 Enterprise Security - JWT authentication, rate limiting, API key management

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🏗️ Architecture

graph TB
    Client[Client Applications]

    subgraph "API Layer"
        Gateway[API Gateway<br/>REST + gRPC]
    end

    subgraph "Routing Layer"
        Router[Model Router<br/>Intelligent Routing]
    end

    subgraph "Inference Layer"
        Orchestrator[Inference Orchestrator<br/>Model Server Integration]
        Triton[Triton Inference Server<br/>ONNX/PyTorch Models]
    end

    subgraph "Async Processing"
        Queue[Kafka/RabbitMQ]
        Worker[Batch Worker<br/>Async Inference]
    end

    subgraph "Data Layer"
        Metadata[Metadata Service<br/>Model Registry]
        Postgres[(PostgreSQL)]
        Redis[(Redis Cache)]
        S3[(Object Storage)]
    end

    subgraph "Observability"
        Prometheus[Prometheus]
        Jaeger[Jaeger]
        Logs[Structured Logs]
    end

    Client --> Gateway
    Gateway --> Router
    Gateway --> Queue
    Router --> Orchestrator
    Orchestrator --> Triton
    Queue --> Worker
    Worker --> Triton
    Worker --> S3

    Gateway --> Metadata
    Router --> Metadata
    Metadata --> Postgres
    Metadata --> Redis

    Gateway -.-> Prometheus
    Router -.-> Prometheus
    Orchestrator -.-> Prometheus
    Worker -.-> Prometheus

    Gateway -.-> Jaeger
    Router -.-> Jaeger
    Orchestrator -.-> Jaeger

Loading

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📁 Project Structure

distributed-ai-platform/
├── services/                    # Microservices
│   ├── api-gateway/            # Entry point, auth, rate limiting
│   ├── model-router/           # Intelligent request routing
│   ├── inference-orchestrator/ # Model server integration
│   ├── batch-worker/           # Async job processing
│   └── metadata-service/       # Model registry
├── models/                      # ML models and configs
│   └── sample-classifier/      # Example ONNX model
├── k8s/                        # Kubernetes manifests
│   ├── base/                   # Base configurations
│   └── overlays/               # Environment-specific
├── docker/                     # Dockerfiles
├── scripts/                    # Utility scripts
│   ├── loadtest/              # Load testing
│   └── setup/                 # Environment setup
├── docs/                       # Documentation
├── tests/                      # Integration tests
└── .github/workflows/          # CI/CD pipelines

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🚀 Quick Start

Prerequisites

• Go 1.21+
• Docker & Docker Compose
• Kubernetes (minikube/kind for local)
• Python 3.9+ (for model preparation)

Local Development

# Clone the repository
git clone <repo-url>
cd distributed-ai-platform

# Start all services locally
docker-compose up -d

# Verify services are running
docker-compose ps

# Submit a test inference request
curl -X POST http://localhost:8080/v1/infer \
  -H "Authorization: Bearer demo-token" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "resnet18",
    "version": "v1",
    "input": {
      "image": "base64_encoded_image_data"
    }
  }'

Kubernetes Deployment

# Deploy to local cluster
kubectl apply -k k8s/overlays/dev

# Port-forward API gateway
kubectl port-forward svc/api-gateway 8080:80

# Watch autoscaling
kubectl get hpa -w

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🔧 Services

API Gateway

Port: 8080
Purpose: Entry point for all requests

• REST and gRPC endpoints
• JWT authentication
• Rate limiting (Redis-backed)
• Request validation

Endpoints:

• POST /v1/infer - Real-time inference
• POST /v1/batch - Submit batch job
• GET /v1/jobs/{id} - Check job status
• GET /health - Health check

Model Router

Port: 8081
Purpose: Intelligent request routing

• Multiple routing strategies (round-robin, least-latency, canary)
• Model version management
• Circuit breakers per backend
• Health tracking

Inference Orchestrator

Port: 8082
Purpose: Model server integration

• Triton Inference Server client
• Retry with exponential backoff
• Timeout handling
• Latency tracking

Batch Worker

Purpose: Async job processing

• Kafka consumer
• Worker pool with backpressure
• Result persistence (PostgreSQL + S3)
• Graceful shutdown

Metadata Service

Port: 8083
Purpose: Model registry

• Model CRUD operations
• Version management
• PostgreSQL + Redis caching
• Schema validation

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📊 Observability

Metrics (Prometheus)

Access at http://localhost:9090

Key Metrics:

• inference_request_duration_seconds - Request latency histogram
• inference_requests_total - Request counter by model/version
• inference_errors_total - Error counter
• batch_job_duration_seconds - Batch job processing time
• cache_hit_rate - Metadata service cache efficiency

Tracing (Jaeger)

Access at http://localhost:16686

• End-to-end request tracing
• Service dependency visualization
• Performance bottleneck identification

Logging

Structured JSON logs with correlation IDs:

{
  "level": "info",
  "ts": "2026-02-02T19:30:00Z",
  "caller": "handler/inference.go:45",
  "msg": "inference request completed",
  "correlation_id": "abc-123",
  "model": "resnet18",
  "version": "v1",
  "duration_ms": 45,
  "status": "success"
}

---

🧪 Testing

Unit Tests

# Run all unit tests
make test

# With coverage
make test-coverage

Integration Tests

# Start test environment
docker-compose -f docker-compose.test.yml up -d

# Run integration tests
make test-integration

Load Testing

# Install k6
brew install k6  # or appropriate package manager

# Run load test
k6 run scripts/loadtest/inference.js

# Expected: 1000 RPS, p95 < 100ms

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🔐 Security

• Authentication: JWT tokens or API keys
• Rate Limiting: Token bucket algorithm (100 req/min default)
• Input Validation: Schema-based validation
• Secrets Management: Kubernetes secrets
• Network Policies: Service-to-service encryption ready

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📈 Performance

Benchmarks (local environment):

Metric	Value
Throughput	1000+ RPS
P50 Latency	25ms
P95 Latency	85ms
P99 Latency	150ms

Scaling:

• Horizontal pod autoscaling based on CPU and custom metrics
• Supports 10,000+ concurrent connections
• Batch processing: 100+ jobs/second

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🛠️ Development

Adding a New Model

1. Export model to ONNX:

# models/your-model/export_model.py
import torch
model = YourModel()
torch.onnx.export(model, dummy_input, "model.onnx")

2. Create Triton config:

# models/your-model/config.pbtxt
name: "your-model"
platform: "onnxruntime_onnx"
max_batch_size: 8

3. Register in metadata service:

curl -X POST http://localhost:8083/v1/models \
  -d '{
    "name": "your-model",
    "version": "v1",
    "framework": "onnx",
    "endpoint": "triton:8001"
  }'

Building Services

# Build all services
make build

# Build specific service
cd services/api-gateway && go build -o bin/api-gateway

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🚢 Deployment

CI/CD Pipeline

GitHub Actions workflow:

1. Lint & Test - golangci-lint, unit tests
2. Build - Multi-arch Docker images
3. Security Scan - gosec, trivy
4. Deploy to Staging - Automatic on merge to main
5. Deploy to Production - Manual approval

Environment Variables

Variable	Description	Default
PORT	Service port	8080
LOG_LEVEL	Logging level	info
DB_HOST	PostgreSQL host	localhost
REDIS_HOST	Redis host	localhost
KAFKA_BROKERS	Kafka brokers	localhost:9092
TRITON_URL	Triton server URL	localhost:8001

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📚 Documentation

• Architecture Deep Dive
• API Reference
• Deployment Guide
• Contributing Guide

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🎓 Learning Outcomes

This project demonstrates:

✅ Backend Engineering

• Microservices architecture
• RESTful and gRPC APIs
• Database design and caching strategies

✅ Distributed Systems

• Service discovery and load balancing
• Circuit breakers and retry logic
• Graceful degradation

✅ MLOps

• Model serving infrastructure
• Version management
• A/B testing and canary deployments

✅ DevOps

• Containerization and orchestration
• CI/CD pipelines
• Infrastructure as Code

✅ Observability

• Metrics, tracing, and logging
• Performance monitoring
• Debugging distributed systems

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📝 License

MIT License - see LICENSE file for details

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🤝 Contributing

Contributions welcome! Please read CONTRIBUTING.md first.

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📧 Contact

For questions or feedback, please open an issue.

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Built with ❤️ using Go, Kubernetes, and modern cloud-native technologies