# Serverless ML Deployment: From Notebook to API

> Source: <https://promptcube3.com/en/threads/3604/>
> Published: 2026-07-26 08:01:38+00:00

# Serverless ML Deployment: From Notebook to API

`.pkl`

or `.h5`

file sitting in a Jupyter notebook, you can bypass the entire MLOps nightmare by using serverless containers. This approach removes the need to manage OS patches or scale clusters manually.## The Infrastructure Gap

Traditional deployment is a slog: you provision a VM, fight with dependency versions, write a wrapper API, containerize it, and then set up a load balancer. Serverless flips this. You provide the image, and the cloud provider handles the scaling and execution.

**Deployment Speed:** Minutes vs. days.**Cost Efficiency:** Pay-per-request means zero cost for idle models.**Scalability:** Automatic scaling during traffic spikes without manual intervention.**Maintenance:** No OS updates or hardware monitoring.

## Real-World Deployment Workflow

To move from a notebook to a live endpoint in under 10 minutes, you need your model file, a basic FastAPI wrapper, and a cloud CLI (like `gcloud`

for Google Cloud Run) configured.

### 1. Environment Setup

Keep your model file (e.g.,

`my_model.pkl`

) in a dedicated project folder. Create a `requirements.txt`

to lock your versions, as mismatching scikit-learn versions between training and deployment is the #1 cause of crashes.

```
fastapi
uvicorn
scikit-learn==1.3.0
pandas
```

### 2. The API Wrapper

You need a lightweight entry point. FastAPI is the standard here because of its speed and automatic Swagger documentation.

``` python
from fastapi import FastAPI
import joblib
import pandas as pd

app = FastAPI()
model = joblib.load("my_model.pkl")

@app.post("/predict")
def predict(data: dict):
    df = pd.DataFrame([data])
    prediction = model.predict(df)
    return {"prediction": prediction.tolist()}
```

### 3. Containerization & Deployment

Since serverless platforms like Google Cloud Run or AWS App Runner require a container, you'll need a simple Dockerfile.

```
FROM python:3.9-slim
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]
```

Once the image is built, a single CLI command pushes it to the cloud and gives you a public HTTPS URL. This is the most practical tutorial for anyone who wants to focus on prompt engineering or model tuning rather than managing Kubernetes clusters. For a deep dive into optimizing these containers for faster cold starts, check out the documentation at promptcube3.com.

[Next ML Production Failures: Why Models Crash After Deployment →](/en/threads/3587/)