Machine learning model development is only half the battle. The real challenge lies in deploying these models to production environments where they can deliver real business value. In this comprehensive guide, we'll explore the entire ML lifecycle from development to deployment. We'll cover essential topics like model versioning, containerization with Docker, CI/CD pipelines for ML, monitoring model drift, and scaling inference services. Whether you're using TensorFlow, PyTorch, or Scikit-learn, these principles apply universally. I'll share insights from my experience deploying models that handle 10K+ daily predictions with 99.7% uptime, including real-world examples from healthcare diagnostics and energy consumption forecasting projects.