Deploying a machine learning model involves several key steps to ensure it runs efficiently in production. Here's a concise guide:

1. Prepare the Model

  • Export the trained model in a compatible format (e.g., .pkl, .h5, or .onnx).
  • Validate model performance with test data.
    👉 Example: Model Preparation

2. Containerization with Docker

  • Package the model and dependencies into a Docker container.
  • Use Dockerfile to define the environment.
  • 📄 Dockerfile Template

3. Deploy to Cloud Platform

  • Choose a cloud provider (e.g., AWS, GCP, or Azure).
  • Set up a server or use serverless functions.
  • 🌐 Cloud Deployment Guide

4. Monitor and Maintain

  • Track model performance with tools like Prometheus or ELK Stack.
  • Schedule retraining with CI/CD pipelines.
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For advanced topics, explore Model Serving with Flask or AutoML Deployment. 📚