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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| ```markdown | |
| # Premium AI Marketing Generator Deployment Guide | |
| ## 🚀 Quick Start | |
| 1. **Install Dependencies** | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 2. **Run Sample Implementation** | |
| ```bash | |
| python ai_marketing_model.py | |
| ``` | |
| ## 💼 Freelancer Business Model | |
| ### Pricing Tiers | |
| - **Starter**: $2,500 (Basic personalization + 3 content types) | |
| - **Professional**: $4,500 (Advanced segments + A/B testing) | |
| - **Enterprise**: $7,500 (Full automation + API integration) | |
| ### Client Deliverables | |
| - Custom-trained AI models | |
| - Integration with client CRM/ERP systems | |
| - Real-time content generation API | |
| - Performance dashboard with ROI tracking | |
| ### Scalability Features | |
| - **Async Processing**: Handle 1000+ simultaneous requests | |
| - **GPU Optimization**: 5-10x faster generation | |
| - **Auto-scaling**: Cloud deployment ready | |
| ## 📊 Performance Metrics | |
| - **Content Quality**: Coherence, relevance, brand alignment | |
| - **Customer Engagement**: Click-through rates, conversions | |
| - **ROI Tracking**: Revenue attribution per campaign | |
| ## 🎯 Target Clients | |
| - E-commerce stores ($1M+ revenue) | |
| - Marketing agencies | |
| - Enterprise retail brands | |
| ## 🔧 Technical Requirements | |
| - Python 3.8+ | |
| - 8GB+ RAM | |
| - GPU recommended for training | |
| - PostgreSQL for customer data | |
| ``` |