Create a Python code template using Hugging Face Transformers and scikit-learn to build a generative AI model that produces marketing content (e.g., email campaigns or social media posts) for e-commerce businesses. Integrate a predictive component that analyzes user data (e.g., purchase history CSV) to forecast customer preferences and tailor the generated text accordingly. Include fine-tuning on a dataset like GPT-2 or Llama, with evaluation metrics for coherence and accuracy. Make it automation-ready for freelancers charging premium rates, with examples for handling surged demand in personalized experiences. Output the full code, explanations, and sample usage.
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| # AI Forge Technical Architecture | |
| ## Core Components | |
| ### 1. No-Code Studio | |
| - **Drag-and-drop interface** for assembling AI pipelines | |
| - **Template Marketplace**: Pre-built industry solutions (marketing/healthcare/e-commerce) | |
| - **Visual Workflow Builder**: Node-based editing of data flows and model interactions | |
| ### 2. Model Types Supported | |
| - **Text Generation**: GPT-4/Claude 2/Llama 2 fine-tuning | |
| - **Image Generation**: Stable Diffusion/DALL·E pipelines | |
| - **Predictive Models**: Scikit-learn/PyTorch/TensorFlow automl | |
| - **Code Generation**: Fine-tuned Codex models | |
| ### 3. Backend Services | |
| mermaid | |
| graph TD | |
| A[Client] --> B[API Gateway] | |
| B --> C[Authentication] | |
| B --> D[Project Management] | |
| B --> E[Model Training] | |
| B --> F[Prediction Serving] | |
| C --> G[Auth0/Ory Hydra] | |
| D --> H[PostgreSQL] | |
| E --> I[Kubernetes Job Queue] | |
| F --> J[FastAPI Servers] | |
| I --> K[GPU Workers] | |
| J --> L[Redis Cache] | |
| ## Tech Stack | |
| ### Frontend | |
| - **React** with TypeScript | |
| - **Tailwind CSS** for styling | |
| - **React Flow** for workflow visualization | |
| - **Vanta.js** for interactive backgrounds | |
| ### Backend | |
| - **Python** with FastAPI | |
| - **Celery** for async task queue | |
| - **Ray** for distributed training | |
| - **PostgreSQL** for metadata | |
| - **Redis** for caching | |
| ### AI/ML Infrastructure | |
| - **Hugging Face Transformers** | |
| - **ONNX Runtime** for optimized inference | |
| - **MLflow** for experiment tracking | |
| - **Seldon Core** for model serving | |
| ## Data Flow | |
| 1. **Ingestion**: CSV uploads, DB connectors (Postgres/MySQL), API integrations (Salesforce/Zapier) | |
| 2. **Preprocessing**: Auto-cleaning, anonymization (HIPAA/GDPR), feature engineering | |
| 3. **Training**: Distributed on Kubernetes with GPU/TPU support | |
| 4. **Serving**: REST/gRPC endpoints with auto-scaling | |
| 5. **Monitoring**: Drift detection, performance metrics | |
| ## Scalability Features | |
| - **Autoscaling**: Kubernetes HPA for prediction servers | |
| - **Spot Instances**: Cost-effective GPU training | |
| - **Model Caching**: Frequently used models kept warm | |
| - **Edge Deployment**: Export to ONNX/TensorRT | |
| ## Compliance | |
| - **Data Encryption**: AES-256 at rest, TLS 1.3 in transit | |
| - **Access Control**: RBAC with JWT claims | |
| - **Audit Logs**: All actions recorded in SIEM | |
| - **Compliance Certifications**: SOC2 Type II, HIPAA, GDPR | |
| ## Deployment Options | |
| 1. **Cloud**: AWS/GCP/Azure (1-click deployment) | |
| 2. **Hybrid**: On-prem GPU cluster + cloud management | |
| 3. **Edge**: Export as Docker container for local deployment | |
| ## Freelancer Features | |
| - **Template Licensing**: Sell custom workflows in marketplace | |
| - **White-labeling**: Rebrand models for client delivery | |
| - **API Monetization**: Charge per prediction call | |
| - **Collaboration**: Shared project spaces | |
| </html> |