Model Deployment

Until now, we have focused on building accurate machine learning models. We cleaned data, engineered features, tuned hyperparameters, and organized everything using pipelines.

However, a trained model has no real value unless it can be used by real users or systems.

This lesson explains Model Deployment — the process of making a trained model available for predictions in real-world applications.

What Is Model Deployment?

Model deployment means taking a trained machine learning model and integrating it into a production environment.

Once deployed, the model can receive new data, process it using the same pipeline, and return predictions automatically.

In simple words: deployment turns a model into a usable service.

Why Deployment Matters

In practice, companies do not run models in notebooks.

They deploy models to:

• Approve loans automatically • Detect fraud in real time • Recommend products to users • Predict customer behavior

Without deployment, machine learning remains only an experiment.

Deployment Flow Using Our Dataset

We continue using the same dataset (Dataplexa ML Housing & Customer Dataset) to maintain consistency.

The deployment flow looks like this:

1. Train pipeline 2. Save trained model 3. Load model later 4. Accept new input data 5. Return predictions

Saving the Trained Model

The first step in deployment is saving the trained pipeline to disk.

We use joblib because it is fast and reliable for large models.

import joblib

joblib.dump(pipeline, "loan_approval_model.pkl")

This file now contains:

• Feature scaling logic • Model parameters • Learned weights

Loading the Model for Predictions

Once deployed, the model is loaded whenever predictions are required.

model = joblib.load("loan_approval_model.pkl")

This step ensures that the exact same model is used across all environments.

Making Predictions on New Data

Now we simulate a real-world scenario.

A new customer applies for a loan. We collect their information and pass it to the model.

import pandas as pd

new_customer = pd.DataFrame([{
    "age": 35,
    "income": 62000,
    "credit_score": 720,
    "loan_amount": 18000,
    "employment_years": 6
}])

prediction = model.predict(new_customer)
prediction

Output:

1 → Loan Approved 0 → Loan Rejected

Real-World Deployment Example

In a banking system, this model would run behind an API.

When a user submits a loan application:

• Data is validated • Pipeline processes the data • Model predicts approval • Decision is returned instantly

All of this happens in milliseconds.

Common Deployment Mistakes

Many ML projects fail after deployment due to simple mistakes.

Common issues include:

• Using different preprocessing steps • Changing feature order • Not saving pipelines correctly • Manual data transformations

Pipelines prevent most of these problems.

Mini Practice

Try changing the customer income and observe how the prediction changes.

This helps you understand how sensitive the model is to input features.

Exercises

Quick Quiz

In the next lesson, we learn how to track deployed models and detect performance issues using Model Monitoring.