In this article, you’ll learn how to package a trained machine learning model behind a clean, well-validated HTTP API using FastAPI, from training to field testing and basic production validation.
Topics we will cover include:
- Training, saving, and loading the scikit-learn pipeline for inference
- Building a FastAPI application with strong input validation with Pydantic
- Expressing, evaluating, and validating hypotheses about health assessment
Let’s explore these strategies.
Machine Learning Task Guide for Modeling with FastAPI
Photo by the Author
When you train a machine learning model, a common question comes up: “How do we actually use it?” This is where many machine learning experts get stuck. Not because posting is difficult, but because it is often misinterpreted. Shipping is not about uploading a .pkl file and hopefully it works. It means allowing another system to send data to your model and get predictions. The easiest way to do this is to put your model behind the API. FastAPI make this process easier. It connects machine learning and backend development in a clean way. It is fast, provides automated API documentation with Swagger UIit validates your input data, and keeps the code easy to read and maintain. If you’re already using Python, FastAPI feels natural to work with.
In this article, you will learn how to implement a machine learning model using FastAPI step by step. In particular, you will learn:
- How to train, save, and load a machine learning model
- How to build a FastAPI application and define valid inputs
- How to create and test the conclusion of a projection in the area
- How to add basic productivity features like health checks and dependencies
Let’s get started!
Step 1: Training and Saving the Model
The first step is to train your machine learning model. I’m training a model to learn how different house features influence the final price. You can use any model. Create a file called train_model.py:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 |
enter the pandas like pd from sklearn.linear_model enter LinearRegression from sklearn.pipe enter A pipe from sklearn.preprocessing enter StandardScaler enter joblib # Sample training data data = pd.DataFrame({ “rooms”: [2, 3, 4, 5, 3, 4], “years”: [20, 15, 10, 5, 12, 7], “distance”: [10, 8, 5, 3, 6, 4], “value”: [100, 150, 200, 280, 180, 250] }) X = data[[“rooms”, “age”, “distance”]] y = data[“price”] # Pipeline = preprocessing + model pipe = A pipe([ (“scaler”, StandardScaler()), (“model”, LinearRegression()) ]) pipe.it’s worth it(X, y) |
After training, you must save the model.
|
# Save all pipelines joblib.give up(pipe, “house_price_model.joblib”) |
Now, run the following line in the terminal:
You now have a trained model and preprocessing pipeline, safely stored.
Step 2: Creating a FastAPI App
This is easier than you think. Create a file called main.py:
|
from fast enter FastAPI from pedantic enter BaseModel enter joblib operating system = FastAPI(the subject=“House Price Prediction API”) # Load the model once at startup model = joblib.burden(“house_price_model.joblib”) |
Your model now:
- Loaded once
- Stored in memory
- Good for giving predictions
This is already better than most postings for beginners.
Step 3: Defining What Your Model Expects
This is where most deployments break down. Your model does not accept “JSON.” Accepts numbers in a specific format. FastAPI uses Pydantic to cleanly enforce this.
You may be wondering what Pydantic is: Pydantic is a data validation library that FastAPI uses to ensure that the input your API receives is exactly what your model expects. It automatically checks data types, required fields, and formats before the request reaches your model.
|
class HouseInput(BaseModel): rooms: int years of age: float distance: float |
This does two things for you:
- Validates incoming data
- Automatically writes your API
This ensures no more “why is my model crashing?” wonders.
Step 4: Creating a Predictive Endpoint
Now you have to make your model usable by creating a predictive endpoint.
|
@operating system.posted(“/predict”) def predict_value(data: HouseInput): features = [[ data.rooms, data.age, data.distance ]]
prediction = model.predict(features)
come back { “predicted_value”: all around(prediction[0], 2) } |
That’s your used model. Now you can send a post request and get guesses too.
Step 5: Deploying Your API locally
Run this command in your terminal:
|
uvicorn main:operating system —reload |
Open your browser and go to:
|
http://127.0.0.1:8000/documents |
You will see:
If you’re confused about what that means, you actually see:
- Interactive API documentation
- Form to test your model
- Real time verification
Step 6: Checking the Actual Input
To check it out, click the following arrow:
After this, click on Try it.
Now test it with some data. I use the following values:
|
{ “rooms”: 4, “years”: 8, “distance”: 5 } |
Now, click on Export to get the answer.
The answer is:
|
{ “predicted_value”: 246.67 } |
Your model now accepts real data, returns predictions, and is ready to integrate with apps, websites, or other services.
Step 7: Adding a Health Check
You don’t need Kubernetes on day one, but consider:
- Error handling (bad entries happen)
- Entry predictions
- Versioning your models (/v1/prediction)
- A health check point
For example:
|
@operating system.find out(“/life”) def health(): come back {“condition”: “okay”} |
Simple things like this matter are more than fancy infrastructure.
Step 8: Adding the Requirements.txt file
This step seems small, but it’s one of those things that quietly saves you hours later. Your FastAPI application may be running fine on your machine, but the deployments don’t know what libraries you’ve used unless you tell them. That’s exactly it requirements.txt it belongs to. A simple list of dependencies your project needs to run. Create a file called requirements.txt then add:
|
fast uvicorn scikit–learn the pandas joblib |
Now, whenever anyone has to set up this project, they have to use the following line:
|
pip enter –r requirements.txt |
This ensures the proper functioning of the project without missing packages. The overall structure of the project looks like this:
|
project/ │ ├── train_model.py ├── main.py ├── house_price_model.joblib ├── requirements.txt |
The conclusion
Your model is worthless until someone can use it. FastAPI doesn’t replace the backend developer — it just removes the friction between your model and the real world. And once you’ve released your first model, you stop thinking like a “model trainer” and start thinking like a solution deployer. Please don’t forget to check FastAPI documentation.
