The software industry is rapidly evolving with Artificial Intelligence. Traditional software deployment alone is no longer enough. Modern companies are increasingly building AI-powered systems, intelligent automation platforms, recommendation engines, predictive analytics applications, and machine learning services.

As organizations adopt AI technologies, the demand for engineers who can deploy, automate, scale, and monitor machine learning systems is growing rapidly.

This is where MLOps Engineering becomes one of the most valuable career paths for DevOps professionals.

If you are already working as a DevOps Engineer, you already possess many of the foundational skills required for MLOps Engineering.

What Is Machine Learning?

Before you understand what MLOps is, let’s understand what Machine Learning is and the model it creates.

Machine learning is the process of teaching a program to learn from data. Once the program learns from the data, it creates something called a model. This model contains the knowledge gained from the data. Think of this model as the learned part of the system. After the model is created, we feed it new data, and based on its previous learning, it generates predictions.

For example:

• If you show a model many cat and dog photos, machine learning helps it learn to tell whether a new picture is a cat or a dog.
• If you give a model past sales numbers, machine learning helps it learn to predict next month’s sales.

This is how modern artificial intelligence systems work today.

A model is the learned artifact, similar to a built application artifact/code in software development. We deploy this model as part of AI applications, and it generates predictions for us.

A simple analogy

Think of a model like a house price prediction tool.

• You collect details about houses, such as size, location, and number of rooms.
• You use that historical data to teach the model how price depends on those input data / features.
• The trained model is deployed in production, and then it estimates the price of a new house based on new input data.

Once the model is ready, you can use it to answer questions like “what is this house likely to sell for?” or “is this listing priced too high?”

What Is MLOps?

MLOps is the practice of running machine learning systems and models in production the same way you run software systems and applications in DevOps.

If DevOps helps you build, test, and deploy application code, MLOps helps you build, test, and deploy Machine learning models and the data that feeds them.

For example:

• In DevOps, you deploy a web app and keep it healthy with monitoring and automation.
• In MLOps, you deploy a trained model and keep it healthy with data checks, model versioning, and inference monitoring.

A simple way to think about it: DevOps manages applications. MLOps manages machine learning models and the data they use.

A machine learning service includes: the data used to train the model, the model itself, the process that created the model, and the service that answers prediction requests. So MLOps is about making models work reliably, safely, and repeatably in production.

How it is like DevOps

• DevOps uses version control for code. MLOps uses version control for code, data, and model files.
• DevOps automates build and deploy. MLOps automates data processing, model training, and model deployment.
• DevOps monitors uptime and errors. MLOps monitors prediction quality, data drift, and service performance.

A simple example

Imagine the house price prediction model from the earlier section:

DevOps would deploy the code or API that serves price estimates. MLOps would also make sure the model is retrained when the housing market changes, that the model version is tracked, and that predictions stay accurate.

In other words, MLOps is not a separate skill set. It is DevOps discipline applied to models and machine learning data pipelines.

Typical MLOps Workflow

A typical MLOps workflow explains how a machine learning model moves from data to real-world usage.

Data → Model → Deployment → Monitoring → Improvement

1. Data Collection

First, we collect the data needed to train the model. Example: For a house price prediction model, we collect old house details such as location, size, number of rooms, and selling price.

2. Data Preparation

Raw data is usually not clean. It may have missing values, wrong values, duplicate records, or unwanted columns. So, the data is cleaned and prepared before giving it to the model.

3. Model Training

In this step, the model learns from the prepared data. The model studies the data and finds patterns. For example, it may learn that bigger houses or houses in better locations usually have higher prices.

4. Model Testing

After training, the model is tested to check whether it is giving useful predictions. If the predictions are poor, the team improves the data, changes the approach, and trains again.

5. Model Deployment

Once the model is good enough, it is deployed into a real application. For example, the house price model can be used inside a website or mobile app where users enter house details and get a predicted price. This is similar to DevOps, where an application is moved from development to production.

6. Monitoring

After deployment, the model must be monitored. We check: Is the model working? Is it responding fast? Are the predictions still useful? Has the real-world data changed? This is similar to DevOps monitoring, but in MLOps we also monitor prediction quality.

7. Retraining

Over time, the world changes. For example, house prices may change after a few months. So, the old model may become less accurate. When this happens, the model is trained again with fresh data. This is called retraining.

Simple DevOps Comparison

In DevOps, the flow is usually:

Code → Build → Test → Deploy → Monitor

In MLOps, the flow is usually:

Data → Train → Test → Deploy → Monitor → Retrain

The idea is similar. DevOps keeps software applications running properly. MLOps keeps machine learning models running properly. The main difference is that MLOps also manages data, model accuracy, and retraining.

Who Works on This?

Different people may work on different parts:

• Data engineers prepare the data.
• Data scientists or ML engineers train the model.
• MLOps engineers deploy, automate, and monitor the model.
• Operations teams help keep the system running.

The goal is simple: Keep the machine learning model useful, reliable, and updated in production.

How this compares to DevOps teams

In DevOps, the teams often look like:

• Developers write application code.
• QA tests the code.
• Operations deploy and monitor it.
• Platform/infra teams build the delivery systems.

In MLOps, the teams often look like:

• Data scientists build the model.
• Data engineers prepare the data.
• MLOps engineers deploy and automate the model.
• Operations monitor both service health and model quality.

The important idea is: DevOps and MLOps both require multiple teams working together. The difference is that MLOps adds data and model work to the normal software delivery process.

DevOps vs MLOps

Step-by-Step DevOps to MLOps Roadmap

- Python Basics, Automation Scripting
- File Handling and Virtual Environments

- NumPy, Pandas, and Matplotlib

Data Ingestion → Data Preprocessing → Model Training → Experiment Tracking → Validation → Deployment → Monitoring → Retraining

MLflow · DVC · DagsHub · Kubeflow · Airflow · FastAPI · Docker · Kubernetes · Jenkins · GitHub Actions · TensorFlow · AWS SageMaker · Databricks on AWS

AWS SageMaker · Azure ML · Google Vertex AI

Prometheus · Grafana · ELK Stack · Evidently AI

Why Learners Choose Our MLOps Certification Course

Our MLOps certification course is designed to help learners build skills that work in real-world AI teams.

• Practical-oriented learning designed for real-world MLOps environments
• Access recordings, assignments, labs, notes, and learning resources through our LMS
• Job-oriented curriculum designed to build future-ready AI & MLOps skills
• Learn from industry-experienced mentors with real-world production exposure
• Dedicated learner support for doubt clarification, guidance, and career assistance
• Build real-time projects to gain practical production-level experience

Best Tools for MLOps Engineers

Companies Who Hire MLOps Engineers

MLOps Hiring Demand Is Exploding

There are 3500+ MLOps and related AI infrastructure openings right now. The market is growing faster than the number of qualified engineers who can deliver ML systems in production.

• Companies are urgently looking for people who can deploy, automate, and monitor machine learning models.
• Very few engineers have true MLOps experience today.
• If you learn now, you become an early adopter in the AI era.

Why this matters

MLOps is the bridge between AI research and real business results. The engineers who understand both cloud infrastructure and machine learning workflows are in short supply. This is your chance to move ahead of the competition, join the AI era early, build the skills companies need today, and become one of the few qualified MLOps engineers in the market.

This transition will also boost your salary package as companies pay a premium for MLOps and AI infrastructure skills.

Learn now, because the AI era has already started. The sooner you join, the faster you become the trusted expert teams need.

DevOps Engineer to MLOps Engineer Salary Trends

Approximate salary ranges vary by experience and company.

Global demand for AI Infrastructure and MLOps Engineers is increasing rapidly.

Career Opportunities After Learning MLOps

Possible career paths include:

Final Thoughts

The future of engineering is becoming AI-driven. DevOps Engineers already possess many of the core skills required in modern MLOps environments. By learning Python, Machine Learning Basics, ML Deployment, Kubernetes for AI, ML Monitoring, and CI/CD for ML, you can transition into one of the most promising engineering careers in the AI industry.

MLOps is becoming the bridge between DevOps, Cloud Infrastructure, Automation, and Artificial Intelligence.

🚀 The best time to start preparing for this transition is now. Think MLOps is like DevOps 12 years back. Those who adapted DevOps 12 years back, they are successfully enjoying the career. MLOps is going to be the future. Be future ready.

Frequently Asked Questions (FAQs)