Last updated on June 3rd, 2025 at 11:57 am
Are You Still Struggling to Launch Machine Learning Model Deployment Live?
Ever trained a brilliant model but couldn’t figure out how to get it into users’ hands? You’re not alone. Many data science learners and early professionals get stuck between “training” and “deployment.” Knowing how to deploy machine learning models effectively is what turns your code into something valuable and deployable.
In this beginner’s guide, we’ll walk you through a complete process to deploy ML models on AWS EC2 from scratch. You’ll also learn how the Programme in Data Science and Artificial Intelligence helps bridge this skill gap.
Why Machine Learning Model Deployment Matters in Real Projects
Training a model is half the battle. If it just sits on your laptop, it serves no one. Model deployment helps you:
- Make real-time predictions
- Allow others to use your model via a web or mobile app
- Turn your portfolio project into a product
- Learn backend basics that hiring managers love
With cloud platforms like AWS EC2, this is easier than ever, even if you’re just getting started.
Understand EC2: The Backbone of AWS Deployment
Amazon EC2 (Elastic Compute Cloud) gives you resisable computing capacity in the cloud. That means you can rent a server, use it like your computer, and run ML apps on it.
Amazon Elastic Compute Cloud is a service within Amazon Web Services (AWS) that enables users to rent virtual servers to run their own computing applications.
Common AWS EC2 Instance Types for ML Workloads
| EC2 Instance Type | Best For | Features |
|---|---|---|
| t2.micro | Beginners, testing | Free tier, low performance |
| g4dn.xlarge | GPU-based inference | NVIDIA GPU, 16 GB RAM |
| m5.large | General-purpose workloads | 8 GB RAM, 2 vCPUs |
| p3.2xlarge | Deep learning training | Powerful GPU, expensive |
| c5.large | High performance compute | Best for CPU-intensive tasks |
Choosing the right EC2 type depends on your budget and task type. Most beginners start with t2.micro or m5.large.
Step-by-Step Guide to Machine Learning Model Deployment on AWS EC2
According to data from Synergy Research Group, enterprises worldwide spent $94 billion on cloud infrastructure services in the first quarter of 2025.
1. Train Your ML Model Locally
Train your model in Jupyter Notebook or VS Code. Export it using joblib or pickle.
You’ll need:
- Preprocessed data
- Trained model file (e.g., model.pkl)
- Dependencies list (requirements.txt)
2. Set Up Your EC2 Instance
- Sign into your AWS account
- Launch an EC2 instance using Ubuntu 20.04
- Select the appropriate AWS ec2 instance types
- Add security group rules to allow HTTP (port 80) and SSH (port 22)
- Generate .pem key for SSH access
This setup gives you a virtual Linux server in the cloud.
3. Access Your EC2 Server via SSH
Use this command:
ssh -i “your-key.pem” ubuntu@<ec2-ip-address>
4. Install Python and Dependencies
Inside EC2, run:
sudo apt update
sudo apt install python3-pip
pip install flask joblib scikit-learn
Upload your model and script using scp or GitHub. Create a Python file (app.py) to load the model and handle predictions.
5. Build a Simple Flask API
Here’s a basic example:
from flask import Flask, request, jsonify
import joblib
app = Flask(__name__)
model = joblib.load(‘model.pkl’)
@app.route(‘/predict’, methods=[‘POST’])
def predict():
data = request.get_json(force=True)
prediction = model.predict([data[‘input’]])
return jsonify(prediction.tolist())
6. Host Your Flask App with Gunicorn or Nginx
You can run the Flask server using:
gunicorn –bind 0.0.0.0:80 app:app
This lets you expose the API to external users.
7. Test Your Model’s Endpoint
Use Postman or cURL to send test requests:
curl -X POST http://<ec2-ip>/predict -H “Content-Type: application/json” -d ‘{“input”: [5.1, 3.5, 1.4, 0.2]}’
You should see your model’s prediction in response.
Master Real-World ML Skills with Imarticus Learning
If you’re looking to go beyond basic Python scripts and truly master machine learning model deployment, the Executive Post Graduate Programme in Data Science and Artificial Intelligence by Imarticus Learning is a strong place to start. This 11-month course, offered in association with AWS, helps professionals future-proof their careers with a curriculum infused with cutting-edge GenAI technologies.
Rather than focusing only on theory, this programme takes a project-first approach. You’ll begin by working on real-world business challenges and learn new tools and concepts just in time, exactly when you need them. With over 35 tools and projects included, the hands-on learning experience is one of the strongest features.
What makes it even more valuable is the global capstone project, where you’ll solve a real business problem from an international startup. Completing this project earns you a capstone certificate, which adds credibility to your CV and LinkedIn profile.
You’ll also benefit from dedicated career support. Imarticus Learning provides assistance with CV building, mock interviews, and exclusive job portal access to help you land the right role faster. Plus, the AI and Cloud Conclave gives you an opportunity to connect face-to-face with industry leaders and broaden your professional network.
Learning happens over the weekend through interactive online sessions, ideal if you’re working full-time but still want to upskill with the latest advancements in AI, cloud, and machine learning deployment.
If you’re serious about deploying machine learning models on platforms like AWS EC2 and want career-ready skills, enrol in the Executive Post Graduate Programme in Data Science and Artificial Intelligence by Imarticus Learning. Build, test, and deploy with confidence to get ready for the future.
Enrol in the Executive Post Graduate Programme in Data Science and Artificial Intelligence today and start deploying your models like a pro.
FAQ
- What is AWS EC2 used for in machine learning?
AWS EC2 provides scalable cloud computing power to run and deploy machine learning models efficiently. - How to deploy machine learning models on AWS EC2?
Train your model, launch an EC2 instance, install dependencies, and host your model using a Flask API. - Which AWS EC2 instance types are good for ML?
GPU-based types like g4dn or p3 are best for training; m5 or t2 work well for basic model deployment. - Can beginners use AWS for machine learning model deployment?
Yes, AWS offers a Free Tier with t2.micro that’s ideal for learning model deployment basics. - What is the benefit of learning AWS EC2 for data science?
It teaches cloud deployment, a crucial skill for transitioning ML models into production. - Does the Programme in Data Science and Artificial Intelligence teach model deployment?
Yes, it includes hands-on projects using AWS and GenAI technologies for end-to-end learning. - How long does it take to deploy a model on EC2?
With basic Python and cloud knowledge, a simple deployment can take under an hour.
Is AWS EC2 secure for machine learning deployments?
Yes, with proper security groups and key management, EC2 provides a safe environment.
