Chapters
Data Science Workflow: From Problem to Solution – A Complete Step-by-Step Journey for Beginners
📗 Chapter 9: Model Deployment and Integration
From Notebook to Real-World Application: Delivering
Your Model to Users
🧠 Introduction
You’ve built and tuned a great model. But in the real world,
it's not useful until people can interact with it — that's where deployment
comes in.
Model deployment is the bridge between a Jupyter Notebook
and real-world impact.
This chapter will help you:
- Understand
deployment methods (batch, API, web app)
- Build
and deploy a machine learning model using Flask
- Save,
load, and serve your model
- Explore
integration options (web, cloud, production pipelines)
- Monitor
your deployed model for real-time performance
🚀 1. What Is Model
Deployment?
Model deployment is the process of integrating a
trained machine learning model into an application (web, mobile, API) where it
can make real-time or batch predictions.
🧩 2. Types of Deployment
|
Type |
Description |
Use Case Example |
|
Batch |
Run predictions on
bulk data |
Weekly customer churn
scoring |
|
Real-Time API |
Get
predictions on-the-fly |
Recommending
products on a website |
|
Embedded |
Integrated into
another application |
Mobile ML apps or firmware
models |
|
Stream-based |
Works with
real-time data streams |
Fraud
detection in transactions |
📦 3. Saving and Loading
Your Model
✅ Save Model
python
import
joblib
joblib.dump(model,
'model.pkl')
✅ Load Model
python
model
= joblib.load('model.pkl')
🛠️ 4. Deploying a Model
with Flask
Flask is a lightweight web framework perfect for deploying
ML models as APIs.
📁 Folder Structure:
pgsql
project/
│
├── app.py
├── model.pkl
├── templates/
│ └── index.html
✅ app.py
python
from
flask import Flask, request, jsonify, render_template
import
joblib
import
numpy as np
app
= Flask(__name__)
model
= joblib.load('model.pkl')
@app.route('/')
def
home():
return render_template('index.html')
@app.route('/predict',
methods=['POST'])
def
predict():
features = [float(x) for x in
request.form.values()]
final_features = [np.array(features)]
prediction = model.predict(final_features)
return render_template('index.html',
prediction_text=f'Prediction: {prediction[0]}')
if
__name__ == '__main__':
app.run(debug=True)
✅ index.html
html
<!DOCTYPE
html>
<html>
<head>
<title>Prediction App</title>
</head>
<body>
<h2>Enter input values</h2>
<form action="/predict"
method="post">
<input name="feature1"
type="text" />
<input name="feature2"
type="text" />
<input name="feature3"
type="text" />
<button
type="submit">Predict</button>
</form>
<h3>{{ prediction_text }}</h3>
</body>
</html>
Run it using:
bash
python
app.py
Now go to http://127.0.0.1:5000 in your browser.
🌍 5. Deploy to the Cloud
Once tested locally, you can deploy to:
|
Platform |
Features |
Free Tier |
|
Heroku |
Easy Flask deployments |
✅ |
|
Render |
Fast CI/CD
for web APIs |
✅ |
|
AWS EC2 |
Full control over
environment |
❌ |
|
Google Cloud Run |
Docker-based
scalable APIs |
✅ |
|
Azure App Service |
Web apps and APIs |
✅ |
Example: Deploy to Heroku
- Install
Heroku CLI
- Create
requirements.txt:
bash
pip
freeze > requirements.txt
- Create
Procfile:
bash
echo
"web: python app.py" > Procfile
- Push
to Heroku:
bash
heroku
create
git
init
heroku
git:remote -a your-app-name
git
add .
git
commit -m "initial commit"
git
push heroku master
🔄 6. Integration Options
✅ Web Interface
Use:
- Flask
+ HTML (as shown above)
- Streamlit
for quick dashboards
✅ REST API
Serve predictions programmatically using JSON:
python
@app.route('/api',
methods=['POST'])
def
api():
data = request.get_json(force=True)
prediction =
model.predict([np.array(list(data.values()))])
return jsonify({'prediction':
prediction[0]})
📊 7. Model Monitoring
Deployed models need to be tracked and updated.
What to Monitor:
|
Metric |
Description |
|
Accuracy/Precision |
Is the model still
performing well? |
|
Latency |
How fast are predictions
returned? |
|
Drift detection |
Is the input data
distribution changing? |
|
Failure logs |
Any
unexpected inputs or crashes? |
Tools:
- MLflow
- Prometheus
+ Grafana
- Sentry
- AWS
SageMaker Monitor
🔄 8. Updating Your Model
Retrain with new data periodically:
python
#
retrain_model.py
X_new,
y_new = get_new_data()
model.fit(X_new,
y_new)
joblib.dump(model,
'model.pkl')
Use CI/CD tools like GitHub Actions or Jenkins to
auto-deploy updates.
📋 Summary Table:
Deployment Toolkit
|
Task |
Tool/Platform |
|
Save/Load model |
joblib, pickle |
|
Build API |
Flask,
FastAPI |
|
Host app/API |
Heroku, Render, AWS |
|
Create UI |
HTML,
Streamlit |
|
Monitor model |
MLflow, Grafana |
|
Retrain/deploy pipeline |
GitHub
Actions, Airflow |
FAQs
1. What is the data science workflow, and why is it important?
Answer: The data science workflow is a structured step-by-step process used to turn raw data into actionable insights or solutions. It ensures clarity, efficiency, and reproducibility from problem definition to deployment.
2. Do I need to follow the workflow in a strict order?
Answer: Not necessarily. While there is a general order, data science is iterative. You may go back and forth between stages (like EDA and feature engineering) as new insights emerge.
3. What’s the difference between EDA and data cleaning?
Answer: Data cleaning prepares the dataset by fixing errors and inconsistencies, while EDA explores the data to find patterns, trends, and relationships to inform modeling decisions.
4. Is it okay to start modeling before completing feature engineering?
Answer: You can build a baseline model early, but robust feature engineering often improves performance significantly. It's best to iterate and refine after EDA and feature transformations.
5. What tools are best for building and evaluating models?
Answer: Popular tools include Python libraries like scikit-learn, XGBoost, LightGBM, and TensorFlow for building models, and metrics functions within sklearn.metrics for evaluation.
6. How do I choose the right evaluation metric?
Answer: It depends on the problem:
- For
classification: accuracy, precision, recall, F1-score
- For
regression: MAE, RMSE, R²
- Use
domain knowledge to choose the metric that aligns with business goals.
7. What are some good deployment options for beginners?
Answer: Start with lightweight options like:
- Streamlit
or Gradio for dashboards
- Flask
or FastAPI for web APIs
- Hosting
on Heroku or Render is easy and free for small projects.
8. How do I monitor a deployed model in production?
Answer: Use logging for predictions, track performance metrics over time, and set alerts for significant drops. Tools like MLflow, Prometheus, and AWS CloudWatch are commonly used.
9. Can I skip deployment if my goal is just learning?
Answer: Yes. For learning or portfolio-building, it's okay to stop after model evaluation. But deploying at least one model enhances your understanding of real-world applications.
10. What’s the best way to practice the entire workflow?
Answer: Choose a simple dataset (like Titanic or housing prices), go through every workflow step end-to-end, and document your process. Repeat with different types of problems to build experience.
Comments(0)