AI/ML
MLOps
FastAPI for ML Model Serving: Best Practices and Performance Optimization
Learn how to build high-performance ML model APIs using FastAPI, including optimization techniques and deployment strategies.
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7 min read
Why FastAPI
Optimization Tips
FastAPI delivers async performance, type hints, and automatic docs—ideal for ML inference services.
python
from fastapi import FastAPI
app = FastAPI()
@app.get("/predict")
def predict():
return {"ok": True}- Warm model objects at startup
- Batch small requests
- Use async I/O for external calls
- Profile hotspots
Inference Request Flow
sequenceDiagram
Client->>API: /predict
API->>Model: infer()
Model-->>API: result
API-->>Client: JSON
Latency budgets disappear quickly—measure continuously.