Machine Learning Operations Training | MLOps Online Course

Author : siva visualpath21 | Published On : 27 Apr 2026

MLOps vs DevOps: Key Differences You Should Know

Introduction

MLOps is becoming an important part of modern technology, especially as companies use more data and smart systems in their daily work. Many people already know about DevOps, which helps teams build and deliver software faster. But now, with machine learning growing quickly, MLOps has come into the picture. If you are planning to grow your career, understanding the difference between these two is very useful, especially if you are exploring an MLOps Online Course to build future-ready skills.

Let’s break this topic into simple ideas so that anyone, even a beginner, can understand it easily.

What is DevOps?

DevOps is a way of working where developers and operations teams come together. Instead of working separately, they work as one team.

The main goal of DevOps is:

• Build software quickly
• Test it properly
• Release it without delays

Before DevOps, developers created code and passed it to operations teams. This caused delays and errors. DevOps solved this by improving teamwork and automation.

Key Features of DevOps:

• Continuous Integration (CI)
• Continuous Delivery (CD)
• Faster releases
• Better teamwork
• Automation of tasks

In simple words, DevOps helps in building and delivering software smoothly.

What is MLOps?

MLOps stands for Machine Learning Operations. It is similar to DevOps but focuses on machine learning models instead of normal software.

Machine learning models are different because:

• They depend on data
• They keep learning and changing
• They need regular updates

MLOps helps manage this entire process.

Key Features of MLOps:

• Data management
• Model training
• Model testing
• Model deployment
• Monitoring model performance

MLOps ensures that machine learning models work correctly even after they are deployed.

Main Difference between MLOps and DevOps

The biggest difference is what they handle.

• DevOps works with software applications
• MLOps works with machine learning models and data

DevOps focuses on code, while MLOps focuses on both code and data.

How Their Workflows Differ

DevOps Workflow:

1. Write code
2. Test code
3. Deploy application
4. Monitor performance

MLOps Workflow:

1. Collect data
2. Clean and prepare data
3. Train model
4. Test model
5. Deploy model
6. Monitor and retrain

As you can see, MLOps has extra steps because it deals with data and learning systems.

Tools Used in DevOps and MLOps

DevOps tools are mainly used for coding and deployment:

• Jenkins
• Docker
• Kubernetes

MLOps tools include:

• TensorFlow
• MLflow
• Kubeflow

These tools help manage models, data, and performance tracking. Many learners today prefer MLOps Training Online because it covers both development and data handling skills.

Skills Required

DevOps Skills:

• Programming knowledge
• Cloud platforms
• Automation tools
• CI/CD pipelines

MLOps Skills:

• Machine learning basics
• Data handling
• Model building
• Monitoring models

MLOps requires a mix of software and data skills, which makes it slightly more complex than DevOps.

Why MLOps is Growing Fast

Today, companies use AI in many areas like:

• Online shopping
• Banking
• Healthcare
• Social media

Machine learning models must be updated regularly to stay useful. That’s why MLOps is becoming very important.

For example:

• A shopping app recommends products
• A bank detects fraud
• A video app suggests content

All these systems need MLOps to work smoothly.

Challenges in MLOps vs DevOps

DevOps Challenges:

• Managing fast releases
• Keeping systems stable

MLOps Challenges:

• Handling large data
• Model accuracy issues
• Frequent retraining
• Data changes over time

MLOps is more complex because data can change anytime, which affects results.

When to Use DevOps and MLOps

Use DevOps when:

• You are building regular software
• Your application does not depend on learning models

Use MLOps when:

• You are working with AI or machine learning
• Your system depends on data patterns

Many companies now use both together.

Career Opportunities

Both fields offer great career options.

DevOps Roles:

• DevOps Engineer
• Cloud Engineer
• Site Reliability Engineer

MLOps Roles:

• MLOps Engineer
• Machine Learning Engineer
• Data Engineer

If you want to enter the AI field, joining an MLOps Training Course in Chennai can help you learn practical skills and get job-ready.

Why Learning Both is a Smart Choice

Instead of choosing one, learning both gives you an advantage.

You will:

• Understand full system development
• Work on advanced projects
• Get better job opportunities

Companies prefer professionals who can handle both development and machine learning workflows.

FAQs

1. Is MLOps harder than DevOps?

Yes, slightly. MLOps includes data and machine learning, which makes it more complex than DevOps.

2. Can a DevOps engineer become an MLOps engineer?

Yes. With some learning in machine learning and data handling, a DevOps engineer can move into MLOps.

3. Do I need coding for MLOps?

Yes, basic coding knowledge is important, especially in Python.

4. Which has better career growth?

Both have strong demand, but MLOps is growing faster due to AI adoption.

5. Is MLOps only for big companies?

No. Even small companies are now using machine learning, so MLOps is needed everywhere.

Conclusion

MLOps and DevOps are both important in today’s tech world, but they serve different purposes. DevOps focuses on software delivery, while MLOps handles machine learning systems and data. Understanding both can open many career opportunities and help you stay ahead in the industry.