📖 Deploying Generative AI Models¶

🧠 Why Deployment is Different for GenAI
⚙️ Local Deployment Options
🐳 Dockerizing a GenAI App
🌐 Serving with FastAPI or Flask
☁️ Cloud Deployment Patterns
⚡ Performance + Monitoring
🛡️ Production Risks and Mitigation
🔚 Closing Notes

🧠 Why Deployment is Different for GenAI¶

⚖️ Compute and Latency Tradeoffs¶

🔄 Stateless vs. Stateful Generations¶

🧠 Model Size, Token Limits, and Cost Constraints¶

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⚙️ Local Deployment Options¶

💻 Run LLMs on Your Machine (GPU/CPU)¶

⚙️ Using Transformers + Text Generation Pipeline¶

🔧 Quantization + Model Acceleration (e.g. bitsandbytes, GGUF)¶

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🐳 Dockerizing a GenAI App¶

🧱 Folder Structure and Dependencies¶

🐳 Dockerfile for Hugging Face Model¶

🚀 Run Container Locally with API Endpoint¶

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🌐 Serving with FastAPI or Flask¶

⚙️ REST API with POST Endpoint¶

💬 Endpoint for Text Generation¶

🔒 Basic Auth, Rate Limiting, CORS¶

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☁️ Cloud Deployment Patterns¶

🌍 Hugging Face Inference API¶

🔧 Hosting via Spaces (Streamlit/Gradio)¶

☁️ Deploy on AWS/GCP/Azure¶

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⚡ Performance + Monitoring¶

📊 Token Throughput and Latency¶

🔍 Logging Inputs and Outputs¶

📈 OpenTelemetry / Prometheus (optional)¶

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🛡️ Production Risks and Mitigation¶

🧨 Prompt Injection Protection¶

🔁 Response Filtering / Red-teaming¶

🔒 Security & Privacy Considerations¶

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🔚 Closing Notes¶

🔁 Summary and Deployment Recap¶

🧠 When to Use Local vs. Cloud¶

🚀 Beyond Notebooks: Launching Real Apps¶

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