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
- Ingestion: CSV uploads, DB connectors (Postgres/MySQL), API integrations (Salesforce/Zapier)
- Preprocessing: Auto-cleaning, anonymization (HIPAA/GDPR), feature engineering
- Training: Distributed on Kubernetes with GPU/TPU support
- Serving: REST/gRPC endpoints with auto-scaling
- 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
- Cloud: AWS/GCP/Azure (1-click deployment)
- Hybrid: On-prem GPU cluster + cloud management
- 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