A model sitting in a Jupyter notebook doesn’t help anyone. It only starts paying off once someone else — a web app, a mobile client, a nightly batch job — can actually call it and get a prediction back. This phase is about closing that gap: saving a trained model to disk, wrapping it behind an API, giving it a UI, packaging it so it runs the same way everywhere, and then watching it in production so it doesn’t quietly go stale.

What “deployment” means in ML

Deployment is the process of making a trained model usable by real users/systems.

This usually includes:

• packaging preprocessing + model together
• exposing predictions via API or batch jobs
• monitoring data quality and model performance

Phase 10 topics

1. Saving and Loading Models (Pickle, Joblib)
2. Building an ML API with Flask/FastAPI
3. Deploying ML Models to Streamlit
4. Dockerizing an ML Application
5. Monitoring Model Drift

A production ML loop

  flowchart LR
  D[New data] --> P[Preprocess]
  P --> M[Model]
  M --> O[Predictions]
  O --> U[Users/Systems]
  U --> L[Logs + outcomes]
  L --> R[Retrain]
  R --> M

Next

Start with Saving and Loading Models (Pickle, Joblib) — before you can serve a model anywhere, you need a reliable way to get it out of your training script and onto disk.