Introduction

Building a Machine Learning model is only part of the process. To make it useful in real-world applications, you need to deploy it. Deployment allows users to interact with your model through applications or websites.

In this lesson, you will learn how to deploy models built using Machine Learning with Flask and create a simple API.

What is Model Deployment?

Model deployment is the process of integrating a trained Machine Learning model into a production environment so it can make predictions on new data.

Example

A house price prediction model used on a real estate website.

What is Flask?

Flask is a lightweight Python web framework used to build web applications and APIs.

Why Flask?

• Simple and easy to learn
• Lightweight
• Perfect for ML model deployment
• Flexible

Deployment Workflow

1. Train and save the model
2. Create a Flask application
3. Load the model in Flask
4. Create API endpoints
5. Send input and get predictions

Step 1: Save the Model

import pickle

pickle.dump(model, open(“model.pkl”, “wb”))

Step 2: Create Flask App

from flask import Flask, request, jsonify
import pickle

app = Flask(name)

model = pickle.load(open(“model.pkl”, “rb”))

Step 3: Create Prediction API

@app.route(‘/predict’, methods=[‘POST’])
def predict():
 data = request.get_json()
 features = data[‘input’]
 prediction = model.predict([features])
 return jsonify({‘prediction’: prediction.tolist()})

Step 4: Run Application

if name == “main“:
 app.run(debug=True)

Step 5: Test API

Send a POST request with JSON input:

{
“input”: [1000, 3]
}

You will receive a prediction as output.

Key Concepts Used

• Model serialization (pickle)
• API development
• HTTP requests
• JSON data handling

Real-World Applications

• Web applications
• Mobile apps
• Business dashboards
• AI-powered tools

Deployment Alternatives

• Cloud platforms (AWS, Azure, GCP)
• Docker containers
• FastAPI for faster APIs

Best Practices

• Validate input data
• Handle errors properly
• Optimize model performance
• Secure your API

Conclusion

Model deployment is a crucial step that turns your Machine Learning model into a real-world product. Flask provides a simple and effective way to deploy models quickly.

In the next lesson, you will learn how to build a strong resume and prepare for Machine Learning interviews.

FAQs

What is model deployment?

It is the process of making a model available for real-world use.

Why use Flask for deployment?

Because it is simple and lightweight.

What is pickle used for?

It is used to save and load trained models.

Can ML models be deployed without Flask?

Yes, using other frameworks like FastAPI or cloud services.

Is deployment important for jobs?

Yes, it is a key skill for ML engineers.

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