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FastAPI Uç Noktasıyla Tahmin Sunma

Bir joblib modelini JSON yükünü kabul edip tahmin döndüren bir FastAPI POST rotasına saracak, ardından bir curl isteğiyle test edeceksiniz.

FastAPI Uç Noktasıyla Tahmin Sunma, CoddyKit'te ücretsiz bir Machine Learning Academy dersidir. Bu, 4 dersinin 3. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, Machine Learning Academy öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. Machine Learning Academy kursu toplamda 4 dersten oluşur.

Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.

From Notebook to Production API

A Jupyter notebook is a great development environment but a terrible production serving system. The standard path from notebook to production is: train and save a model with joblib, wrap it in a REST API, and deploy that API as a containerised service. The API accepts raw feature values as JSON, preprocesses them through the fitted pipeline, and returns predictions in milliseconds.

Why FastAPI for ML Serving?

FastAPI is a modern Python web framework built on Pydantic and Starlette. It generates automatic interactive documentation (Swagger UI), validates request bodies with type hints, and handles async I/O efficiently. For ML serving, FastAPI is popular because it requires very little boilerplate, supports concurrent requests via async workers, and integrates naturally with Python data types used in sklearn and pandas.

Installing FastAPI and Uvicorn

FastAPI requires uvicorn as the ASGI server to run it. Install both with a single command. uvicorn is a high-performance async server that handles HTTP connections and passes requests to the FastAPI application. In production, you would typically run uvicorn behind an nginx reverse proxy with multiple worker processes.

# pip install fastapi uvicorn[standard]

# Verify installation
from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn

print('FastAPI ready')

Defining the Request Schema with Pydantic

Pydantic BaseModel classes define the structure of incoming requests. FastAPI uses these models to automatically validate JSON bodies — if a required field is missing or has the wrong type, FastAPI returns a clear 422 error before your code even runs. Each field in the Pydantic model corresponds to one input feature for the model.

from pydantic import BaseModel
from typing import Optional

class IrisFeatures(BaseModel):
    sepal_length: float
    sepal_width: float
    petal_length: float
    petal_width: float

class PredictionResponse(BaseModel):
    predicted_class: int
    class_name: str
    confidence: float

# Example input (FastAPI will validate this automatically)
input_data = IrisFeatures(sepal_length=5.1, sepal_width=3.5,
                           petal_length=1.4, petal_width=0.2)
print('Input:', input_data)

Loading the Model at Startup

Load the model once at startup, not on every request. Loading a joblib file on every prediction would add hundreds of milliseconds of latency per request. Use a module-level variable or a FastAPI lifespan event handler to load the model when the server starts and keep it in memory for all subsequent requests.

import joblib
from contextlib import asynccontextmanager
from fastapi import FastAPI

ml_models = {}

@asynccontextmanager
async def lifespan(app: FastAPI):
    # Startup: load model once
    ml_models['iris'] = joblib.load('/tmp/iris_pipeline.joblib')
    print('Model loaded at startup')
    yield
    # Shutdown: cleanup if needed
    ml_models.clear()

app = FastAPI(title='Iris Predictor API', lifespan=lifespan)

Creating the Prediction Endpoint

Define a POST route that accepts the Pydantic input model, converts it to a NumPy array, calls pipeline.predict and predict_proba, and returns the prediction as a structured JSON response. FastAPI serialises Pydantic response models automatically.

import numpy as np
from fastapi import FastAPI
from pydantic import BaseModel
import joblib

app = FastAPI()
model = None

@app.on_event('startup')
def load_model():
    global model
    model = joblib.load('/tmp/iris_pipeline.joblib')

CLASS_NAMES = ['setosa', 'versicolor', 'virginica']

@app.post('/predict')
def predict(features: IrisFeatures):
    X = np.array([[features.sepal_length, features.sepal_width,
                   features.petal_length, features.petal_width]])
    pred = int(model.predict(X)[0])
    proba = float(model.predict_proba(X)[0].max())
    return {
        'predicted_class': pred,
        'class_name': CLASS_NAMES[pred],
        'confidence': round(proba, 4)
    }

Adding a Health Check Endpoint

A /health or /ping endpoint is essential for production services. Load balancers and orchestration systems (Kubernetes, ECS) call this endpoint periodically to confirm the service is alive. A healthy response means the server is running AND the model is loaded. Return 503 if the model failed to load.

from fastapi import FastAPI
from fastapi.responses import JSONResponse

app = FastAPI()

@app.get('/health')
def health():
    if model is None:
        return JSONResponse(status_code=503,
                            content={'status': 'unhealthy', 'reason': 'model not loaded'})
    return {'status': 'ok', 'model': 'iris_pipeline', 'version': '1.0.0'}

@app.get('/')
def root():
    return {'message': 'Iris Predictor API — POST /predict to get a classification'}

Running the Server Locally

Save the FastAPI app to main.py and start it with uvicorn main:app --reload. The --reload flag auto-restarts on file changes (development only). Navigate to http://localhost:8000/docs to see the auto-generated Swagger UI where you can test predictions interactively.

# Save to main.py then run:
# uvicorn main:app --host 0.0.0.0 --port 8000 --reload

# Test with curl:
# curl -X POST http://localhost:8000/predict \
#   -H 'Content-Type: application/json' \
#   -d '{"sepal_length": 5.1, "sepal_width": 3.5, "petal_length": 1.4, "petal_width": 0.2}'
#
# Expected response:
# {"predicted_class": 0, "class_name": "setosa", "confidence": 0.9981}

print('Command to start: uvicorn main:app --reload --port 8000')

Testing the Endpoint with the requests Library

In a test script or notebook, use requests.post to call your running API. This is also how client applications (mobile apps, dashboards, other microservices) consume the prediction API. The same request format works from any language — curl, JavaScript fetch, Go's http.Client.

import requests

url = 'http://localhost:8000/predict'
payload = {
    'sepal_length': 6.3,
    'sepal_width': 3.3,
    'petal_length': 6.0,
    'petal_width': 2.5
}

response = requests.post(url, json=payload)
if response.status_code == 200:
    result = response.json()
    print('Predicted class:', result['class_name'])
    print('Confidence:', result['confidence'])
else:
    print('Error:', response.status_code, response.text)

Input Validation and Error Handling

FastAPI's Pydantic validation catches type errors automatically, but you should also handle model-level errors (e.g., unexpected NaN values, out-of-range inputs). Use try/except inside the route function and return a 400 or 500 with a meaningful error message. Avoid leaking internal error details (stack traces) to API callers in production.

from fastapi import FastAPI, HTTPException
import numpy as np

app = FastAPI()

@app.post('/predict')
def predict(features: IrisFeatures):
    try:
        X = np.array([[features.sepal_length, features.sepal_width,
                       features.petal_length, features.petal_width]])
        if np.any(np.isnan(X)) or np.any(X < 0):
            raise HTTPException(status_code=400,
                                detail='Input contains invalid values (NaN or negative)')
        pred = int(model.predict(X)[0])
        proba = float(model.predict_proba(X)[0].max())
        return {'predicted_class': pred, 'confidence': round(proba, 4)}
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail='Internal prediction error')

Batch Prediction Endpoint

For high-throughput use cases, add a batch endpoint that accepts a list of feature sets and returns a list of predictions in one API call. Batching reduces network overhead and allows the model to vectorise predictions efficiently (sklearn predict handles matrices).

from typing import List
from pydantic import BaseModel
import numpy as np

class BatchRequest(BaseModel):
    instances: List[IrisFeatures]

@app.post('/predict/batch')
def predict_batch(batch: BatchRequest):
    X = np.array([[f.sepal_length, f.sepal_width, f.petal_length, f.petal_width]
                  for f in batch.instances])
    preds = model.predict(X).tolist()
    probas = model.predict_proba(X).max(axis=1).tolist()
    return {'predictions': [{'class': p, 'confidence': round(c, 4)}
                            for p, c in zip(preds, probas)]}

Quick Check

Test your understanding of serving ML predictions with FastAPI from this lesson.

Lesson Recap

In this lesson you learned: FastAPI wraps a joblib-loaded pipeline into a typed REST endpoint with automatic JSON validation and Swagger documentation, load the model once at startup to avoid per-request disk I/O latency, and always include a /health endpoint so load balancers and orchestrators can verify the service is alive. Next up we add prediction logging to the API and discuss data drift and model retraining triggers.

Sıkça Sorulan Sorular

“FastAPI Uç Noktasıyla Tahmin Sunma” dersi ücretsiz mi?

Evet — “FastAPI Uç Noktasıyla Tahmin Sunma” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve Machine Learning Academy kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. Machine Learning Academy kursu toplamda 4 dersten oluşur.

“FastAPI Uç Noktasıyla Tahmin Sunma” dersinde ne öğreneceğim?

Bir joblib modelini JSON yükünü kabul edip tahmin döndüren bir FastAPI POST rotasına saracak, ardından bir curl isteğiyle test edeceksiniz. Machine Learning Academy ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.

Machine Learning Academy öğrenmeye başlamak için deneyim gerekli mi?

Önceden deneyim gerekmez. CoddyKit'te Machine Learning Academy, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 3. dersidir.

“FastAPI Uç Noktasıyla Tahmin Sunma” dersi ne kadar sürer?

Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.

Bu Machine Learning Academy dersinde kod yazıp çalıştırabilir miyim?

Evet. Her Machine Learning Academy dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.

Bu kursun tüm dersleri

  1. Modelleri joblib ve pickle ile Kaydetme
  2. Modelleri Sürümlendirme: Dosya Adları ve Üst Veriler Neden Önemlidir
  3. FastAPI Uç Noktasıyla Tahmin Sunma
  4. Tahminleri İzleme: Girdileri ve Çıktıları Günlüğe Kaydetme
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