predict_with_latest_model.py

def load_latest_model() -> sklearn.pipeline.Pipeline:
    latest_model_path = pd.read_csv("metadata_store.csv").query("timestamp == timestamp.max()")["model_path"].values[0]
    loaded_model = joblib.load(latest_model_path)
    return loaded_model

def model_predict(user_id: str) -> float:
    model = load_latest_model()
    features = feature_store.get_user_feature(user_id)
    return model.predict([[v for k,v in features.items() if k != "target"]])
    
model_predict("a27b0912-0cf8-11ed-899e-b29c4abd48f4")