MLOps: Bridging the Gap Between ML Models and Deployment

MLOps streamlines the deployment and monitoring of ML models in production.

Tools like Docker, Kubernetes, and TensorFlow Serving are commonly used.

Example: Serving a Model with FastAPI

from fastapi import FastAPI
import pickle

app = FastAPI()
model = pickle.load(open("model.pkl", "rb"))

@app.post("/predict/")
def predict(data: dict):
    prediction = model.predict([data["features"]])
    return {"prediction": prediction.tolist()}

This creates a simple API to serve ML model predictions.