What should I use to move from notebook to production AI app?

If you’re moving from a notebook to a production AI app, the right tool depends on what part of production you mean:

Best general answer

Use a workflow/API framework plus deployment tooling, not just the notebook.

Common path

1. Refactor notebook code into Python modules
2. Wrap the model in an API using:
   • FastAPI for most AI apps
   • Flask if you want something simpler, but FastAPI is usually better
3. Add app orchestration / UI if needed:
   • Streamlit or Gradio for quick internal apps
   • Next.js / React for customer-facing apps
4. Containerize with Docker
5. Deploy to:
   • Cloud Run, AWS ECS/Fargate, Kubernetes, or Azure App Service
6. Add MLOps/monitoring:
   • MLflow for experiment tracking/model registry
   • Weights & Biases for experiments
   • Prometheus/Grafana or your cloud monitoring
   • Evidently AI for drift/data quality

If you’re building an LLM app specifically

You’ll likely want:

• FastAPI for backend
• LangChain or LlamaIndex if you need retrieval/tooling/agent workflows
• Vector DB like Pinecone, Weaviate, Milvus, pgvector
• Redis for caching and rate limiting
• Postgres for persistence
• Docker + CI/CD for deployment

If you need model serving for custom ML models

Consider:

• BentoML: very good for packaging and serving models
• Ray Serve: good for scalable distributed serving
• Seldon or KServe: more Kubernetes-native, enterprise style
• TorchServe / TF Serving: if you’re serving PyTorch/TensorFlow models specifically

Practical recommendation

If you want the simplest solid production stack:

• Python + FastAPI
• Docker
• Postgres
• Redis
• Cloud Run or ECS
• MLflow for tracking
• Prometheus/Grafana or cloud logs for monitoring

Quick rule of thumb

• Prototype notebook → Streamlit/Gradio
• Production API → FastAPI + Docker
• Production model serving → BentoML or Ray Serve
• Production LLM app → FastAPI + LangChain/LlamaIndex + vector DB

If you tell me:

1. whether it’s an LLM app or a custom ML model, and
2. whether you need internal tool or customer-facing production,
   I can suggest a specific stack.