Practical Exercises in Model Deployment

In this final section, we will engage in practical exercises that involve exporting and serving scikit-learn models, simulating live inference, and integrating monitoring and update strategies. These exercises are designed to consolidate our understanding of model deployment pipelines and demonstrate best practices for real-world ML operations. By the end of these exercises, we will have applied the full lifecycle of model deployment using scikit-learn.

Exercise 1: Saving and Reloading a Model Pipeline for Deployment

In this exercise, we will serialize a trained model pipeline and reload it to simulate a production environment. This exercise ensures that preprocessing and inference logic are bundled together and reusable across environments.

Implementation steps:

1. Load libraries
2. Create and train pipeline:
3. Save and reload pipeline
4. Generate new predictions:

Exercise 2: Monitoring Model Accuracy Over Time

This exercise involves simulating streaming...