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AI System Architecture Design

AI System Architecture Design for Unity AI Beta Program

Version: 1.0
Date: 2023-10-15

1. Overview

The Unity AI Beta Program establishes an open AI ecosystem connecting creators with AI tools to accelerate RT3D content creation. This architecture supports:

  • Scalable integration of AI tools via APIs
  • Secure user registration and beta management
  • Real-time content processing for global users
  • Extensibility for future AI models and services

2. Architecture Diagram

+-------------------+     +---------------------+     +----------------------+
|   Creator Clients |     | Unity AI Gateway    |     | AI Tool Marketplace  |
| (Unity Editor/Web)|<--->| (API Gateway)       |<--->| (3rd-party AI Tools) |
+-------------------+     +----------+----------+     +----------------------+
                                     |
                            +--------v--------+     +-----------------------+
                            | Core Services   |     | AI Processing Engine  |
                            | (Microservices) |<--->| (TensorFlow/Kubeflow) |
                            +--------+--------+     +-----------------------+
                                     |
                     +---------------+----------------+
                     | Data Layer                     |
                     | (PostgreSQL/MongoDB/Blob Store)|
                     +--------------------------------+

3. Technology Stack

3.1 Core Infrastructure

  • Cloud Platform: Microsoft Azure (Global reach, AI/ML integrations)
  • Container Orchestration: Kubernetes 1.27 (AKS for autoscaling)
  • Service Mesh: Istio 1.17 (traffic management, security)
  • API Gateway: Azure API Management 4.0 (OAuth2, rate limiting)

3.2 AI Processing

  • AI Runtime: TensorFlow 2.12 + PyTorch 2.0 (model inference)
  • Orchestration: Kubeflow 1.7 (ML pipeline management)
  • Tool Integrations: gRPC APIs (protobuf serialization)

3.3 Data Layer

  • Structured Data: PostgreSQL 15 (user profiles, beta metadata)
  • Unstructured Data: MongoDB 6.0 (AI tool catalog, content metadata)
  • Blob Storage: Azure Blob Storage (RT3D assets, AI outputs)
  • Cache: Redis 7.0 (session/store)

3.4 Security

  • Auth: Azure Active Directory (OIDC/SAML) + JWT tokens
  • Encryption: AES-256 (at rest), TLS 1.3 (in transit)
  • Compliance: GDPR, CCPA via Azure Policy

4. Key Components

4.1 Unity AI Gateway

  • Role: Central entry point for creators and AI tools.
  • Features:
    • REST/GraphQL APIs (versioned endpoints: /v1/ai/tools)
    • Dynamic routing to AI tools using Istio VirtualServices
    • Rate limiting (1,000 RPM/user) and DDoS protection

4.2 Core Services (Microservices)

  • Beta Management Service:
    • Manages user enrollment, waitlists, and feedback (stored in PostgreSQL).
    • Integrates with SendGrid API for email notifications.
  • Tool Integration Service:
    • Validates and routes requests to AI tools via gRPC.
    • Uses Azure Service Bus for async job queuing.
  • Content Processing Service:
    • Parallelizes AI tasks (e.g., texture generation, NPC behavior) using Celery.
    • Outputs stored in Azure Blob Storage with CDN caching.

4.3 AI Processing Engine

  • Deployment:
    • AI models containerized with Docker 23.0.
    • Auto-scaling via Kubernetes HPA (CPU/GPU metrics).
  • Supported Tasks:
    • Asset generation (GANs), code automation (Codex-like), physics simulation.
  • Monitoring: Prometheus/Grafana for latency/error tracking.

5. Implementation Steps

Phase 1: Foundation (8 Weeks)

  1. Set up AKS cluster with Istio service mesh.
  2. Deploy PostgreSQL (user data) and MongoDB (tool registry).
  3. Implement OAuth2.0 auth flow via Azure AD.

Phase 2: Core Services (6 Weeks)

  1. Develop Beta Management Service (Python/Django 4.2).
  2. Build Tool Integration Service (Go 1.20 + gRPC).
  3. Configure Azure Blob Storage with lifecycle policies.

Phase 3: AI Integration (10 Weeks)

  1. Containerize AI models (e.g., NVIDIA Triton for inference).
  2. Deploy Kubeflow pipelines for batch processing.
  3. Integrate 3rd-party tools via standardized protobuf schemas.

Phase 4: Security & Scaling (4 Weeks)

  1. Enable end-to-end TLS with Let’s Encrypt.
  2. Configure Istio mTLS for service-to-service encryption.
  3. Stress-test using Locust 2.15 (simulate 10K creators).

6. Scalability & Performance

  • Horizontal Scaling:
    • Kubernetes pods scale to 50 replicas based on CPU/GPU load.
    • Azure Blob Storage scales to 1 PB+ with geo-replication.
  • Performance Targets:
    • API latency < 200ms (p95).
    • AI job processing < 5s (lightweight tasks).
  • CDN: Azure CDN for asset delivery (30% latency reduction).

7. Security Controls

  • Data Isolation: Tenant separation via PostgreSQL schemas.
  • Auditing: Azure Monitor + Log Analytics for traceability.
  • Tool Sandboxing: AI tools run in gVisor containers.
  • Compliance: Annual penetration tests + SOC 2 certification.

8. Future Extensions

  • Edge AI: Deploy lightweight models to Unity Edge via ONNX Runtime.
  • Multi-Cloud: Add GCP/AWS support using Crossplane.
  • Blockchain: Use Ethereum for tool usage tracking (optional).

Character Count: 3,812
This architecture delivers a secure, scalable foundation for Unity’s open AI ecosystem, enabling rapid onboarding of creators and AI tools while ensuring enterprise-grade reliability.