Machine Learning Model Deployment Strategies.

Machine Learning Model Deployment

Deploying ML models to production requires careful consideration of scalability and monitoring.

from flask import Flask, request, jsonify
import pickle
import pandas as pd

app = Flask(__name__)
model = pickle.load(open('model.pkl', 'rb'))

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    df = pd.DataFrame([data])
    prediction = model.predict(df)
    return jsonify({'prediction': prediction.tolist()})

Deployment Options

• Cloud platforms (AWS SageMaker, GCP AI Platform)
• Containerization with Docker
• Serverless functions