AI Model Deployment Tutorial 2026: From Notebook to Production API in 7 Steps

A step-by-step tutorial that takes a trained model from a Jupyter notebook to a production API.

## AI Model Deployment Tutorial 2026 Most models die in notebooks. This tutorial walks the exact path from a trained PyTorch model to a production API, using tools with real star counts from 2026-07-31. ### Step 1: Export the model Save weights and architecture. For LLMs use GGUF or safetensors; for vision models export to ONNX. ### Step 2: Choose serving technology - **vLLM** (87,794 stars) for LLMs: high throughput, continuous batching - **TorchServe** or **Ray Serve** for PyTorch models - **ONNX Runtime** for cross-platform inference ### Step 3: Write a minimal server [python] from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Query(BaseModel): text: str @app.post("/predict") def predict(q: Query): return {"result": model_predict(q.text)} [/python] ### Step 4: Containerize with Docker [text] FROM pytorch/pytorch:2.4-cuda12.1 COPY model/ /app/model/ COPY server.py /app/ CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "8000"] [/text] ### Step 5: Deploy - Kubernetes (kubectl apply) for scale - Or serverless: Modal, RunPod, or AWS Lambda for spiky traffic ### Step 6: Add monitoring Track latency, token usage, and error rates with Langfuse (28k+ stars) or Prometheus + Grafana. ### Step 7: Version and rollback Tag every model version, keep the previous container warm, and automate rollback on error-rate spikes. ### Real numbers A typical vLLM deployment serves 100+ concurrent requests on one A100 with continuous batching; TTFT stays under 500ms. ### FAQ **How long does deployment take?** First deployment 2-5 days for a small team; subsequent ones hours with CI/CD. **CPU or GPU?** Start CPU for small models under 7B quantized; GPU for production LLMs.