Disponibilização de previsões com um endpoint FastAPI
Os alunos encapsularão um modelo joblib em uma rota POST do FastAPI que aceita uma carga JSON e retorna uma previsão, testando-a depois com uma solicitação curl.
Disponibilização de previsões com um endpoint FastAPI é uma aula grátis de Machine Learning Academy no CoddyKit. Esta é a aula 3 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de Machine Learning Academy, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de Machine Learning Academy inclui 4 aulas no total.
Partes desta aula ainda não foram traduzidas e aparecem em inglês.
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.
Perguntas Frequentes
A aula “Disponibilização de previsões com um endpoint FastAPI” é grátis?
Sim — o texto completo de “Disponibilização de previsões com um endpoint FastAPI” é grátis para ler aqui na web. Para praticá-la interativamente (um editor de código integrado e um tutor de IA 24/7) e desbloquear o restante do curso de Machine Learning Academy, atualize para CoddyKit PRO. O curso de Machine Learning Academy inclui 4 aulas no total.
O que vou aprender em “Disponibilização de previsões com um endpoint FastAPI”?
Os alunos encapsularão um modelo joblib em uma rota POST do FastAPI que aceita uma carga JSON e retorna uma previsão, testando-a depois com uma solicitação curl. Você pratica Machine Learning Academy com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.
Preciso ter experiência prévia para começar Machine Learning Academy?
Nenhuma experiência prévia é necessária. Machine Learning Academy no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 3 de 4.
Quanto tempo leva a aula “Disponibilização de previsões com um endpoint FastAPI”?
A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.
Posso escrever e executar código nesta aula de Machine Learning Academy?
Sim. Cada aula de Machine Learning Academy inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.
Todas as aulas deste curso
- Salvamento de modelos com joblib e pickle
- Versionamento de modelos: por que os nomes de arquivos e os metadados importam
- Disponibilização de previsões com um endpoint FastAPI
- Monitoramento de previsões: registro de entradas e saídas