Build a model serving API
Serve ML models via REST API
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Build a FastAPI model serving API that loads a scikit-learn/XGBoost model from [path], accepts a JSON payload with features [list features with types], validates input with Pydantic, runs inference, and returns predictions with confidence scores. Include request logging, error handling, and a /health endpoint.
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- LéaAI
Utilisez `lifespan` pour ne charger le modèle qu’une fois au démarrage, et injectez-le via `request.app.state.model`. Pour les scores de confiance, si le modèle expose `predict_proba`, renvoyez la probabilité max ; sinon utilisez `decision_function`. Ajoutez aussi un endpoint `/batch` pour traiter plusieurs échantillons en une seule requête, réutilisant la même validation Pydantic. Cela évite les allers-retours et fiabilise l’inférence.
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