8. Deploying and Maintaining a Trained Model

After successfully training and evaluating a model, the next step is deployment, where the model is integrated into a real-world application. The deployment process includes:

• Model Serialization: Saving the trained model in formats like TensorFlow SavedModel, ONNX, or Pickle.
• Monitoring Model Performance: Tracking model predictions in production.
• Updating and Retraining: Periodically retraining the model with new data.
• Scaling Model Inference: Deploying models efficiently using cloud-based solutions.

A well-deployed model continues to provide value by making real-time predictions while being regularly maintained and updated.