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Integrating Machine Learning Models in FastAPI with Python

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Learn how to integrate machine learning models into your FastAPI applications using Python. In this tutorial, we'll walk through creating a simple FastAPI application and integrating a scikit-learn model for prediction.

First, let's create a FastAPI application. FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints.

```python
from fastapi import FastAPI

app = FastAPI()
```

Now, we'll import our scikit-learn model and prepare data. Ensure you have the necessary dependencies installed.

```python
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import numpy as np

X = np.array([[1, 2], [2, 3], [3, 1]])
y = np.array([2, 3, 1])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LinearRegression()
model.fit(X_train, y_train)
```

Next, we'll create a route to display our model's prediction outcome.

```python
@app.get("/predict/{x1}/{x2}")
async def predict(x1: float, x2: float):
input = np.array([[x1, x2]])
output = model.predict(input)
return {"prediction": output[0]}
```

Finally, run your FastAPI application and test your prediction route.

As a next step, try integrating complex ML models and comparing it to a Flask application or adding more data preprocessing steps.


Additional Resources:
[FastAPI documentation](https://fastapi.tiangolo.com/)
[scikit-learn documentation](https://scikit-learn.org/stable/)
[Handson FastAPI and scikit-learn Tutorial](https://col

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