You gotta have an API

API for Machine Learning

Fast api is pretty awesome for fast ML api. Other options could be mlflow serve.

from fastapi import FastAPI
from pydantic import BaseModel
import joblib

# Initialize the app
app = FastAPI()

# Load the model
model = joblib.load("model.pkl")

# Define a request body using Pydantic BaseModel
class Input(BaseModel):
    feature_1: float
    feature_2: float
    feature_3: float

# Define an endpoint
async def predict(input: Input):
    # Convert input to a dictionary and reshape
    input_dict = input.dict()
    features = [[input_dict["feature_1"], input_dict["feature_2"], input_dict["feature_3"]]]

    # Make a prediction using the model
    prediction = model.predict(features)[0]

    # Return the prediction as a JSON response
    return {"prediction": prediction}

For your model loader - you can easily load a model from a model registry like mlflow or s3 alike. Class Input could be replaced with a datamodel from pydantic for fast feature checking before you use bad data to predict.